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Data Mining in the Medical Field
 
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Video about data mining in the medical field. Made by Aditya Jariwala, Alex Truitt, Tongfei Zhang, and Yishi Xu for Purdue COM 21700 final project, Spring 2017.
Views: 3996 Aditya Jariwala
Data Mining as a healthcare research tool (Analytics Techniques Listed Below)
 
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Here are some additional techniques for data mining: 1. Decision Tree Analysis: https://www.youtube.com/watch?v=bJC5S_ViRCo 2. Text mining in Twitter: https://www.youtube.com/watch?v=I0VCGCnquTQ
A Literature Review on Data Mining Techniques applied in Health Care Decision Making
 
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Literature Review on Data Mining Techniques applied in Health Care Decision Making
Views: 1325 mahesh l
15 Hot Trending PHD Research Topics in Data Mining 2018
 
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15 Hot Trending Data Mining Research Topics 2018 1. Medical Data Mining 2. Education Data Mining 3. Data Mining with Cloud Computing 4. Efficiency of Data Mining Algorithms 5. Signal Processing 6. Social Media Analytics 7. Data Mining in Medical Science 8. Government Domain 9. Financial Data Analysis 10. Financial Accounting Fraud Detection 11. Customer Analysis 12. Financial Growth Analysis using Data Mining 13. Data Mining and IOT 14. Data Mining for Counter-Terrorism Key Research Application Fields: • Crisp-DM • Oracle Data mining • Web Mining • Open NN • Data Warehousing • Text Mining WHY YOU NEED TO OUTSOURCE TO PhD Assistance: a) Unlimited revisions b) 24/7 Admin Support c) Plagiarism Generate d) Best Possible Turnaround time e) Access to High qualified technical coordinators and expertise f) Support: Skype, Live Chat, Phone, Email Contact us: India: +91 8754446690 UK: +44-1143520021 Email: [email protected] Visit Webpage: https://goo.gl/HwJgqQ Visit Website: http://www.phdassistance.com
Views: 5497 PhD Assistance
Health Care Management System - Real Time Project
 
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Title: Health Care Management System - Real Time Project ABSTRACT Health care is one among the major sectors, where we concentrate more and spend lot of time to increase our services to the doctors and patients. Today many health care departments are giving free treatment and medicine to get more popular. In order to monitor the incoming out going patients the application has been designed with a real time implementation. To maintain the privacy the data are stored in the cloud server with a private and public key using cryptographic technique. The application has an advantage of giving rights to access the particular patient details and emergency prevention technique has been handled in order to safe guard the records in the server. EXISTING SYSTEM Many health care management systems will not have emergency prevention technique and will not allow accessing the data directly from the hospital server. Basically health care management system will concentrate on full filling the management details of patient and doctors. PROPOSED SYSTEM In this paper we had three major role players like patient, doctors and lab assistant, we had developed an application in order to reduce the effort of them and increase their quality of service to their customers and in around the hospital in the world. The data will get stored locally in the database with encryption technique and will be updated periodically to the cloud server. Emergency prevention technique has been applied in such a way to safe guard the patient details in the hospital. For more details contact: E-Mail: [email protected] Purchase The Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2017 – 2018 data mining projects 5. 2017 – 2018 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2017 – 2018 ieee titles 8. 2017 – 2018 base paper 9. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2017 – 2018 data mining weka projects 13. 2017 – 2018 b.e projects 14. 2017 – 2018 m.e projects 15. 2017 – 2018 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2017 – 2018 ieee base paper free download 23. 2017 – 2018 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2017 - 2018 48. 2017 - 2018 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects 52. data mining and text mining ieee projects 53. 2017 text mining ieee projects 54. text mining ieee projects 55. ieee projects in web mining 56. 2017 web mining projects 57. 2017 web mining ieee projects 58. 2017 data mining projects with source code 59. 2017 data mining projects for final year students 60. 2017 data mining projects in java 61. 2017 data mining projects for students
Views: 10938 InnovationAdsOfIndia
Talks@12: Data Science & Medicine
 
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Innovations in ways to compile, assess and act on the ever-increasing quantities of health data are changing the practice and police of medicine. Statisticians Laura Hatfield and Sherri Rose will discuss recent methodological advances and the impact of big data on human health. Speakers: Laura Hatfield, PhD Associate Professor, Department of Health Care Policy, Harvard Medical School Sherri Rose, PhD Associate Professor, Department of Health Care Policy, Harvard Medical School Like Harvard Medical School on Facebook: https://goo.gl/4dwXyZ Follow on Twitter: https://goo.gl/GbrmQM Follow on Instagram: https://goo.gl/s1w4up Follow on LinkedIn: https://goo.gl/04vRgY Website: https://hms.harvard.edu/
PHD RESEARCH TOPIC IN DATA MINING
 
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Views: 4781 PHD Projects
Medical records and data driven healthcare
 
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Together with the Australian Commission on Safety and Quality in Healthcare, IHPA has developed this animation to explain the secondary uses of patient medical records. It aims to encourage clear, accurate and complete documentation in patient medical records.
About The Course | Medical Data Mining L01T01 | Introduction & Scientific Background
 
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The Online Certificate Program in Genomics and Biomedical Informatics Bar-Ilan University & Sheba Medical Center Course 803.80-675 - Medical Data Mining Spring 2018 Lecturer: Dr. Ronen Tal-Botzer [email protected] Unit L01: Introduction & Scientific Background Topic 01: About the Course
"Big Data in Healthcare: Utilization and Challenges" - Dr. Carl Asche
 
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How big is "Big Data"? HHS welcomed Dr. Carl Asche for our first lecture in our UICOMP Lecture Series! Dr. Asche is an expert on health economics, outcomes research and epidemiology. He presented on the role of Big Data in healthcare and the issues we need to overcome. More info. about Dr. Asche can be found here: https://pharmacy.uic.edu/people-resources/directory/cva This presentation was based off of a paper by Dr. Asche, which can be accessed here for free via an institutional proxy or login: https://link.springer.com/article/10.1007%2Fs40273-017-0513-5 ABSTRACT Although the analysis of ‘big data’ holds tremendous potential to improve patient care, there remain significant challenges before it can be realized. Accuracy and completeness of data, linkage of disparate data sources, and access to data are areas that require particular focus. This presentation will discuss these areas and shares strategies to promote progress. Improvement in clinical coding, innovative matching methodologies, and investment in data standardization are potential solutions to data validation and linkage problems. Challenges to data access still require significant attention with data ownership, security needs, and costs representing significant barriers to access.
William Paiva: Transforming health care and medical education through clinical Big Data analytics
 
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Health care is undergoing significant transformation, and digital health data is at the center of this change. According to the Centers for Disease Control, nearly 80 percent of the nation’s health care institutions have converted to an electronic medical record (EMR) system from the old paper-based system. New technologies like smartphone applications are also creating new stockpiles of digital data. Genetic data is growing as well; scientists can sequence a person’s entire DNA within 24 hours and for less than $1,000. Collectively, the amount of digital health data is expected to grow from 500,000 to 25 million terabytes over the next five years. Why do we care that our health information is now in a digital format? How does it benefit all of us? People who work in health care—and every industry for that matter—are smart, well trained, and do their best to stay up-to-date with the latest research, methodologies and trends. However, it is not rational to assume individuals have the depth of knowledge or data access to deal with every situation they encounter. Furthermore, the health care field is already understaffed, and this issue will only get worse as the looming mass retirement of baby boomers from the health care workforce creates an unprecedented supply-and-demand crisis. Digitized health data has the potential to help mitigate this troubling situation. Predictive medicine uses computing power and statistical methods to analyze EMR and other health-related data to predict clinical outcomes for individual patients. Beyond health outcome forecasting, predictive medicine also can uncover surprising and often unanticipated clinical associations. Oklahoma State University’s Center for Health Systems Innovation (CHSI), through its Institute for Predictive Medicine (IPM), is a leader in the exploding field of predictive medicine thanks to the unprecedented donation by Cerner Corporation of its HIPAA-compliant clinical health database, one of the largest available in the United States. Specifically, this dataset represents clinical information from over 63 million patients and includes admission, discharge, clinical events, pharmacy, and laboratory data spanning more than 16 years. Over 20 full-time CHSI employees and nearly two dozen graduate students are working to execute the CHSI mission to transform rural and Native American health through data analytics. Further, CHSI has a number of ongoing partnerships with academia, health systems and corporations to extract value from digitized health data. One example of CHSI’s numerous predictive medicine projects is an effort to help physicians determine whether the performance of particular cardiovascular drugs varies by gender or race, or both. Conversely, this study will help indicate which drugs perform poorly or even cause complications in these populations. Other CHSI studies are designed to give physicians insight into whether patients with a particular disease are likely to develop or already have an associated disease, which will aid in co-managing these conditions and lead to better health care. Another project is designed to help hospitals use data on patient demographic characteristics, comorbidities, discharge setting, and other medical information contained in comprehensive EMR systems to determine if patients are at high risk for being readmitted for disease-associated complications. If patients are considered high risk, they can get the care and support necessary to prevent frequent cycling through the health care system. Predictive medicine can also lead to the creation and implementation of tools for managing larger patient loads, which can aid health care providers in dealing with supply-and-demand problems. For instance, CHSI has developed a clinical decision support system that can detect diabetic retinopathy with a high degree of accuracy using lab and comorbidity data available through primary care visits. This algorithm addresses the very real challenge of low patient compliance, particularly among rural and underserved populations, with annual ophthalmic eye exams, which are the gold standard for retinopathy detection and preventing vision impairment or total vision loss. CHSI is extending this work to other common diabetes-related microvascular complications with the goal of developing a comprehensive suite of tools that can help increase prevention and management of these complications among the nation’s growing diabetic population.
Views: 1241 Stanford Medicine X
data mining powerpoint
 
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IASP 520 - Data Mining How Data Mining is used in the Healthcare Field
Views: 1046 Stephanie Hansen
The Clustering Problem | Medical Data Mining L02T08 | Unsupervised Machine Learning
 
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The Online Certificate Program in Genomics and Biomedical Informatics Bar-Ilan University & Sheba Medical Center Course 803.80-675 - Medical Data Mining Spring 2018 Lecturer: Dr. Ronen Tal-Botzer [email protected] Unit L02: Unsupervised Machine Learning | From Data to Information to Knowledge Topic 08: The Clustering Problem
AUTOMATIC MEDICAL DISEASE TREATMENT SYSTEM USING DATAMINING
 
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In our proposed system is identifying reliable information in the medical domain stand as building blocks for a healthcare system that is up-todate with the latest discoveries. By using the tools such as NLP, ML techniques. In this research, focus on diseases and treatment information, and the relation that exists between these two entities. The main goal of this research is to identify the disease name with the symptoms specified and extract the sentence from the article and get the Relation that exists between Disease- Treatment and classify the information into cure, prevent, side effect to the user.This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.
Tapping into the Potential of Natural Language Processing in Healthcare
 
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Table of Contents Act 1 - The Possibilities are Endless 01:53 Act 2 - NLP to the Rescue (aka The Hype) 05:14 Act 3 - A Peek Under the Hood (aka The Reality) 16:40 Act 4 - You Can Do It! 25:22 Q&A - 40:10 Gathering insight from clinical notes remains one of the areas of untapped healthcare intelligence with tremendous potential. But extracting that value is difficult. Still, a few organizations across the country are demonstrating success using advanced technology tied to intuitive processes and procedures. Leading one such organizational effort is Wendy Chapman, PhD, chair of the Department of Biomedical Informatics at the University of Utah. Dr. Chapman’s research has driven discovery in new ways to disseminate resources for modeling and understanding information described in narrative clinical reports. Her teams have demonstrated phenotyping for precision medicine, quality improvement, and decision support. Joining Dr. Chapman in a shared discussion is Mike Dow who leads the Natural Language Processing (NLP) technology team at Health Catalyst. Mike and team have several years of experience engaging with a variety of health system organizations across the country who are realizing statistical insight by incorporating text notes along with discrete data analysis. Together, Mike and Dr. Chapman will provide an NLP primer sharing principle-driven stories so you can get going with NLP whether you are just beginning or considering processes, tools or how to build support with key leadership. Learning Objectives: - Understand NLP, both its challenges, and potential to drive clinical insight using social determinants of health - Gain insight into the technology that makes NLP possible - Consider the future potential of NLP View this webin to better understand the potential of NLP through existing applications, the challenges of making NLP a real and scalable solution, and walk away with concrete actions you can take to use NLP for the good of your organization.
Views: 354 Health Catalyst
Healthcare Text Analytics and NLP with Mike Dow
 
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While we are typically focused exclusively on machine learning, this week we will focus on text analytics, a related field that is quickly developing in healthcare using principles of feature engineering. We'll dive into an example of Natural Language Processing (NLP) and how these techniques may benefit the healthcare industry. We are looking forward to having Mike Dow, an NLP industry expert, joining us to talk about opportunities, challenges, and the future.
Views: 8647 Healthcare AI
Big Data Healthcare PROJECTS
 
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Views: 533 PHD Projects
Sentiment Analysis
 
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Welcome to Data Lit! This 3-month course is an intro to data science for beginners. In this video, I'll explain how a popular data science technique called sentiment analysis works using a real-world scenario. We'll play the role of a data scientist working at a startup making a personal healthcare device. Using sentiment analysis, we'll understand how consumers feel about a competitors product. That'll help us make decisions on how to promote our own product, and what feature we can focus on the most. Using Python, Twitter, and Google Colab, anyone can do this process in just a few minutes. Enjoy! Code for this video: https://github.com/llSourcell/Sentiment_Analysis Please Subscribe! And Like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology Join us at the School of AI: https://theschool.ai/ More learning resources: https://towardsdatascience.com/sentiment-analysis-with-python-part-1-5ce197074184 https://www.geeksforgeeks.org/twitter-sentiment-analysis-using-python/ https://www.datacamp.com/community/tutorials/simplifying-sentiment-analysis-python https://www.kaggle.com/ngyptr/python-nltk-sentiment-analysis https://pythonspot.com/python-sentiment-analysis/ https://www.analyticsvidhya.com/blog/2018/07/hands-on-sentiment-analysis-dataset-python/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w #DataLit #SchoolOfAI #SirajRaval Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 51917 Siraj Raval
Data Mining Projects 2016-2017 | ieee data mining papers 2016
 
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ieee data mining papers 2016 for ME,M.Tech.,M.Phil., Ph.D., B.E, B.Tech., MCA A Novel Recommendation Model Regularized with User Trust and Item Ratings Automatically Mining Facets for Queries from Their Search Results Booster in High Dimensional Data Classification Building an intrusion detection system using a filter-based feature selection algorithm Connecting Social Media to E-Commerce: Cold-Start Product Recommendation Using Microblogging Information Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings Crowdsourcing for Top-K Query Processing over Uncertain Data Cyberbullying Detection based on Semantic-Enhanced Marginalized Denoising Auto-Encoder Domain-Sensitive Recommendation with User-Item Subgroup Analysis Efficient Algorithms for Mining Top-K High Utility Itemsets Efficient Cache-Supported Path Planning on Roads Mining User-Aware Rare Sequential Topic Patterns in Document Streams Nearest Keyword Set Search in Multi-Dimensional Datasets Rating Prediction based on Social Sentiment from Textual Reviews Location Aware Keyword Query Suggestion Based on Document Proximity Using Hashtag Graph-based Topic Model to Connect Semantically-related Words without Co-occurrence in Microblogs Quantifying Political Leaning from Tweets, Retweets, and Retweeters Relevance Feedback Algorithms Inspired By Quantum Detection Sentiment Embeddings with Applications to Sentiment Analysis Top-Down XML Keyword Query Processing TopicSketch: Real-time Bursty Topic Detection from Twitter Top-k Dominating Queries on Incomplete Data Understanding Short Texts through Semantic Enrichment and Hashing To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: www.jpinfotech.org, Blog: www.jpinfotech.blogspot.com
Views: 2622 JPINFOTECH PROJECTS
Data Mining Paper Review
 
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Recorded with http://screencast-o-matic.com
Views: 131 venu gopal valeti
Big Data Analytics For Healthcare Final Project Presentation
 
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CSE8803 Big Data Analytics For Healthcare Final Project Presentation Source code available at: https://www.github.com/opme/SurgeonScorecard Research paper at: https://github.com/opme/SurgeonScorecard/blob/master/Surgeon_Scorecard.pdf Sorry to my "Finland Skis" channel subscribers. I needed to upload my final project to youtube for a class I am taking.
Views: 437 Finland Skis
Mining Human Activity Patterns from Smart Home Big Data for Healthcare Applications
 
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Mining Human Activity Patterns from Smart Home Big Data for Healthcare Applications To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org Nowadays, there is an ever-increasing migration of people to urban areas. Health care services is one of the most challenging aspects that is greatly affected by the vast influx of people to city centers. Consequently, cities around the world are investing heavily in digital transformation in an effort to provide healthier ecosystem for people. In such transformation, millions of homes are being equipped with smart devices (e.g. smart meters, sensors etc.) which generate massive volumes of fine-grained and indexical data that can be analyzed to support smart city services. In this paper, we propose a model that utilizes smart home big data as a means of learning and discovering human activity patterns for health care applications. We propose the use of frequent pattern mining, cluster analysis and prediction to measure and analyze energy usage changes sparked by occupants’ behavior. Since people’s habits are mostly identified by everyday routines, discovering these routines allows us to recognize anomalous activities that may indicate people’s difficulties in taking care for themselves, such as not preparing food or not using shower/bath. Our work addresses the need to analyze temporal energy consumption patterns at the appliance level, which is directly related to human activities. For the evaluation of the proposed mechanism, this research uses the UK Domestic Appliance Level Electricity dataset (UK-Dale) - time series data of power consumption collected from 2012 to 2015 with time resolution of six seconds for five houses with 109 appliances from Southern England. The data from smart meters are recursively mined in the quantum/data slice of 24 hours, and the results are maintained across successive mining exercises. The results of identifying human activity patterns from appliance usage are presented in details in this paper along with accuracy of short and long term predictions.
Views: 672 jpinfotechprojects
Health Informatics - An International Journal (HIIJ)
 
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Health Informatics - An International Journal (HIIJ) ISSN : 2319 - 2046 (Online); 2319 - 3190 (Print). http://airccse.org/journal/hiij/index.html Call for papers **************** Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care. The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below: Topics of Interest ****************** Electronic health records Knowledge engineering E-Health Information Information Management in healthcare Expert Systems in Biomdical Communication System in Helthcare Artificial Intelligence in Medicine and Health Sciences Ontological Engineering in Medicine Health Care Information Systems. Knowledge management in Healthcare Clinical Information Systems. Bioinformatics and Biostatistics. Mobile applications for patient care Medical Interoperability Medical Devices and sensors Impact and usability Medical Image Processing and Techniques. ICT in health promotion programmes E-health Guidelines and protocols E-learning and education in healtcare Telemedicine Software, Portals, Devices and Telehealth. Public health and consumer informatics Data Mining and Knowledge Discovery in Medicine. ICT for Patient empowerment ICT for Patient safety Medical Databanks, Databases, and Knowledge Bases. Neurocomputing in Medicine. Healthcare Quality assurance Nursing Informatics Evaluation and technology assessment Home-based eHealth Health Management Issues Health Research Health Economics Issues Paper Submission **************** Authors are invited to submit papers for this journal through E-mail: [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
Views: 18 hiij journal4
Get latest data mining Thesis Topics-+91-9041262727
 
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Views: 99 THESIS HELPS
Deep learning solutions to computational phenotyping in healthcare April 29, 2016
 
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Deep learning solutions to computational phenotyping in healthcare April 29, 2016 Deep Learning | Los Angeles Deep Learning | Los Angeles (Los Angeles, CA) - Meetup www.meetup.com/Deep-Learning-Meetup-Los-Angeles/ For Data Scientists, Data Nerds, Machine Learning Experts, and anyone interested in the field of Deep Learning in Machine Learning architecture and Data ... http://www.meetup.com/Deep-Learning-Meetup-Los-Angeles/ USC ISI Speaker: Zhengping Che (USC) Date: 29 Apr 2016 Time: 3:00 pm - 4:00 pm Location: 11th Floor Large Conference Room [1135] Note: Outside visitors should go to the tenth-floor lobby where they will be met and escorted to the appropriate location five minutes before the talk. Title: Deep learning solutions to computational phenotyping in health care Abstract: Abstract: Exponential growth in electronic healthcare data has resulted in new opportunities and urgent needs to discover meaningful data-driven representations and patterns of diseases. The Recent rise of this research field with more available data and new applications also has introduced several challenges. In this talk, we will present our deep learning solutions to address some of the challenges. First, health care data is inherently heterogeneous, with a variety of missing values and from multiple data sources. We propose variations of Gated Recurrent Unit (GRU) to explore and utilize the informative missingness in health care data, and hierarchical multimodal deep models to utilize the relations between different data sources. Second, model interpretability is not only important but necessary for care providers and clinical experts. We introduce a simple yet effective knowledge distillation approach called interpretable mimic learning to learn interpretable gradient boos! ting tree models while mimicking the performance of deep learning models. Bio: Zhengping Che is a third-year Ph.D. candidate in the Computer Science Department at the University of Southern California, advised by Professor Yan Liu. Before that, he received his bachelor degree in Computer Science from Pilot CS Class (Yao Class) at Tsinghua University, China. His primary research interest lies in the area of deep learning and its applications in the healthcare domain, especially in multivariate time series data. Deep learning | health care data | multimodal deep models
Views: 462 Carl Mullins
Paper (pp. 46-54) Presentation at SIAM Data Mining 2018
 
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Presentation at SIAM Data Mining 2018 Paper title: Mixtures of Block Models for Brain Networks Authors: Zilong Bai, Peter Walker, Ian Davidson DOI: https://epubs.siam.org/doi/10.1137/1.9781611975321.6 Presenter: Zilong Bai (first author of the paper) Thanks to Sikun Li for recording the talk!
Views: 57 zilong bai
Get latest data mining Thesis Topics for master and phd
 
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Get latest data mining thesis topics for master and phd in e2matrix . if you want proper guidance and thesis topics and projects and topics so you can contact to e2matrix . we are provides you proper guidance about your work . so you can't delay and do fast contact to e2matrix . call now : + 91 9041262727, e-mail: [email protected] Just click in the following link and Read more about cloud computing thesis topics http://www.e2matrix.com/blog/2018/07/04/data-mining-research-guidance-and-thesis-topics/ Thanks & Regards E2Matrix http://www.e2matrix.com/ Follow us on Social Media https://www.facebook.com/E2MatrixTrainingAndResearchInstitute/?ref=settings https://twitter.com/e2matrix_lab https://www.instagram.com/e2matrixresearch/
Views: 162 THESIS HELPS
Vista Analytics at the 2017 IEEE International Conference on Big Data
 
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Yihua Shi Astle presents "Application of Dynamic Logistic Regression with Unscented Kalman Filter in Predictive Coding" which is a paper accepted by the conference co-authored with Vista co-founder Craig Freeman and Dr. Xuning Tang
Views: 186 Vista Analytics
Data Mining Research Topics | Data Mining Research Project Topics
 
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Contact Best Matlab Simulations Projects http://matlabsimulations.com/
IHPI Seminar: Understanding healthcare care for the elderly:  impact of patient and providers
 
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December 13, 2018 Understanding healthcare care for the elderly: separating the impact of the patient and the providers Speaker: Speaker: Beth A. Virnig, Ph.D., M.P.H., University of Minnesota and ResDAC The Medicare program provides health insurance for over 95% of people in the US age 65 and older. The continuous quest for improving quality of care and care efficiency has led to the implementation of a range of policies aimed at changing provider and beneficiary behavior. Using Medicare administrative data can provide important insights about the mechanism underlying use patterns and help anticipate the impact of changing incentives. Dr. Virnig is a widely published author of studies examining access to health care and use and outcomes of that care. She examines how health care is influenced by patients, providers, and markets. Her research on the elderly in the Medicare program focuses on cancer surveillance and care, Medicare managed care, and end-of-life care. Dr. Virnig is also the director of the Research Data Assistance Center (ResDAC), which is funded by a contract from CMS to provide free assistance to academic, government, and nonprofit researchers interested in using Medicare or Medicaid data for their research. As Senior Associate Dean of the University of Minnesota School of Public Health, she focuses on faculty development, research development, and strategic planning; and works closely with school leadership to ensure that activities are mission-focused and aimed at building a healthier future.
Views: 469 Michigan Medicine
Research Methods - Introduction
 
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In this video, Dr Greg Martin provides an introduction to research methods, methedology and study design. Specifically he takes a look at qualitative and quantitative research methods including case control studies, cohort studies, observational research etc. Global health (and public health) is truly multidisciplinary and leans on epidemiology, health economics, health policy, statistics, ethics, demography.... the list goes on and on. This YouTube channel is here to provide you with some teaching and information on these topics. I've also posted some videos on how to find work in the global health space and how to raise money or get a grant for your projects. Please feel free to leave comments and questions - I'll respond to all of them (we'll, I'll try to at least). Feel free to make suggestions as to future content for the channel. SUPPORT: —————- This channel has a crowd-funding campaign (please support if you find these videos useful). Here is the link: http://bit.ly/GH_support OTHER USEFUL LINKS: ———————— Channel page: http://bit.ly/GH_channel Subscribe: http://bit.ly/GH_subscribe Google+: http://bit.ly/GH_Google Twitter: @drgregmartin Facebook: http://bit.ly/GH_facebook HERE ARE SOME PLAYLISTS ——————————————- Finding work in Global Health: http://bit.ly/GH_working Epidemiology: http://bit.ly/GH_epi Global Health Ethics: http://bit.ly/GH_ethics Global Health Facts: http://bit.ly/GH_facts WANT CAREER ADVICE? ———————————— You can book time with Dr Greg Martin via Google Helpouts to get advice about finding work in the global health space. Here is the link: http://bit.ly/GH_career -~-~~-~~~-~~-~- Please watch: "Know how interpret an epidemic curve?" https://www.youtube.com/watch?v=7SM4PN7Yg1s -~-~~-~~~-~~-~-
Building Scalable Predictive Modeling Platform for Healthcare Applications, Jimeng Sun
 
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As the adoption of electronic health records (EHRs) has grown, EHRs are now composed of a diverse array of data, including structured information and unstructured clinical progress notes. Two unique challenges need to be addressed in order to utilize EHR data in clinical research and practice: 1) Computational phenotyping: How to turn complex and messy EHR data into meaningful clinical concepts or phenotypes? 2) Predictive modeling: How to develop accurate predictive models using longitudinal EHR data? To address these challenges, I will present our approaches using a case study on early detection for heart failure. For computational phenotyping, we present EHR data as data as inter-connected high-order relations i.e. tensors (e.g. tuples of patient-medication-diagnosis, patient-lab, and patient-symptoms), and then develop expert-guided sparse nonnegative tensor factorization for extracting multiple phenotype candidates from EHR data. Most of the phenotype candidates are considered clinically meaningful and with great predictive power. For predictive modeling, I will present how using deep learning to model temporal relations among events in EHR improved model performance in predicting heart failure (HF) diagnosis compared to conventional methods that ignore temporality.
Views: 419 MMDS Foundation
mining text data projects
 
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Views: 80 PHD Projects
Special Session on GIS in Healthcare   HGIS 2019
 
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Special Session on GIS in Healthcare - HGIS 2019 (ins) AS At Heraklion Heraklion, Greece For more information: https://www.eventbrite.com/e/special-session-on-gis-in-healthcare-hgis-2019-ins-as-tickets-49511877358?aff=QRP DESCRIPTION ================= Special Session on GIS in Healthcare - HGIS 2019 (ins) Within the 5th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM 2019 The need to provide better, personal health information and care at lower costs is opening the way to a number of digital solutions and applications designed and developed specifically for the healthcare. Various open-data initiatives are also making available an enourmous amount of diverse data that needs to be processed and, above all, understood. Under the Digital Health umbrella we find smartphone-based personal health applications, big data reduction and analysis tools, AI-based systems and health information support tools for professional and the industry. The aim of these applications is to help manage cronic conditions or improve ones lifestyle; provide doctors, hospitals and health-services with better, more efficient tools; improve policy makers decision making through the support of data analytics tools. GIS has always provided tools to better understand and contextualize data, this Special session on GIS and Healthcare will show how GIS techniques can be applied to the most common healthcare problems but also explore new areas where GIS can foster healthcare innovation. The main topics include but are not limited to: ==================================================== - GIS for disease control and forecast - GIS for health resources optimization - GIS for heralth emergency support systems - Spatial analysis and data mining for healthcare data - Big data analytics in healthcare - Digital mapping and epidemiology CHAIR =============== Roberto Lattuada myHealthbox Italy Brief Bio Roberto Lattuada holds a PhD in GIS and 3D modelling from the University of London and a Master in Computer Science from the University of Milan. He has more than 15 years of experience in the IT and Mobile industry having worked with companies such as Oracle (US), Vodafone (Italy), Motorola (UK) and T-Mobile International. With a strong experience in strategy and innovation Roberto Lattuada has launched a number of innovative products and services across several industries: he has helped develop the Oracle Spatial suite and he was responsible for the first product supporting geo-imaging within Oracle Spatial, while at Vodafone he was in charge of the first mobile payment solution and at T-Mobile he launched the G1, the first Android-based handset. He is currently CEO of myHealthbox, a company specialized in digital solutions for the healthcare. SCOPE ================ The International Conference on Geographical Information Systems Theory, Applications and Management aims at creating a meeting point of researchers and practitioners that address new challenges in geo-spatial data sensing, observation, representation, processing, visualization, sharing and managing, in all aspects concerning both information communication and technologies (ICT) as well as management information systems and knowledge-based systems. The conference welcomes original papers of either practical or theoretical nature, presenting research or applications, of specialized or interdisciplinary nature, addressing any aspect of geographic information systems and technologies. CONFERENCE AREAS ====================== Each of these topic areas is expanded below but the sub-topics list is not exhaustive. Papers may address one or more of the listed sub-topics, although authors should not feel limited by them. Unlisted but related sub-topics are also acceptable, provided they fit in one of the following main topic areas: 1. DATA ACQUISITION AND PROCESSING 2. REMOTE SENSING 3. MODELING, REPRESENTATION AND VISUALIZATION 4. KNOWLEDGE EXTRACTION AND MANAGEMENT 5. DOMAIN APPLICATIONS Please contact the event manager Marilyn below for the following: - Discounts for registering 5 or more participants. - If your company requires a price quotation. Event Manager Contact: marilyn.b.turner(at)nyeventslist.com You can also contact us if you require a visa invitation letter, after ticket purchase. We can also provide a certificate of completion for this event if required. ---------------------------------- For more information: https://www.eventbrite.com/e/special-session-on-gis-in-healthcare-hgis-2019-ins-as-tickets-49511877358?aff=QRP Eventbrite: https://www.eventbrite.com/o/event-promotions-by-new-york-events-list-11118815675 Twitter: https://twitter.com/nyeventslist Facebook: https://www.facebook.com/NewYorkEventsList New York Events List: http://nyeventslist.com/
Ontologies
 
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Dr. Michel Dumontier from Stanford University presents a lecture on "Ontologies." Lecture Description Ontology has its roots as a field of philosophical study that is focused on the nature of existence. However, today's ontology (aka knowledge graph) can incorporate computable descriptions that can bring insight in a wide set of compelling applications including more precise knowledge capture, semantic data integration, sophisticated query answering, and powerful association mining - thereby delivering key value for health care and the life sciences. In this webinar, I will introduce the idea of computable ontologies and describe how they can be used with automated reasoners to perform classification, to reveal inconsistencies, and to precisely answer questions. Participants will learn about the tools of the trade to design, find, and reuse ontologies. Finally, I will discuss applications of ontologies in the fields of diagnosis and drug discovery. View slides from this lecture: https://drive.google.com/open?id=0B4IAKVDZz_JUVjZuRVpMVDMwR0E About the Speaker Dr. Michel Dumontier is an Associate Professor of Medicine (Biomedical Informatics) at Stanford University. His research focuses on the development of methods to integrate, mine, and make sense of large, complex, and heterogeneous biological and biomedical data. His current research interests include (1) using genetic, proteomic, and phenotypic data to find new uses for existing drugs, (2) elucidating the mechanism of single and multi-drug side effects, and (3) finding and optimizing combination drug therapies. Dr. Dumontier is the Stanford University Advisory Committee Representative for the World Wide Web Consortium, the co-Chair for the W3C Semantic Web for Health Care and the Life Sciences Interest Group, scientific advisor for the EBI-EMBL Chemistry Services Division, and the Scientific Director for Bio2RDF, an open source project to create Linked Data for the Life Sciences. He is also the founder and Editor-in-Chief for a Data Science, a new IOS Press journal featuring open access, open review, and semantic publishing. Please join our weekly meetings from your computer, tablet or smartphone. Visit our website to learn how to join! http://www.bigdatau.org/data-science-seminars
Health Informatics - An International Journal (HIIJ)
 
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Health Informatics - An International Journal (HIIJ) ISSN : 2319 - 2046 (Online); 2319 - 3190 (Print). http://airccse.org/journal/hiij/index.html Call for papers Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care. The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information,and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below: Topics of interest include, but are not limited to, the following: • Electronic health records • Knowledge engineering • E-Health Information • Information Management in healthcare • Expert Systems in Biomdical • Communication System in Helthcare • Artificial Intelligence in Medicine and Health Sciences • Ontological Engineering in Medicine • Health Care Information Systems. • Knowledge management in Healthcare • Clinical Information Systems. • Bioinformatics and Biostatistics. • Mobile applications for patient care • Medical Interoperability • Medical Devices and sensors • Impact and usability • Medical Image Processing and Techniques. • ICT in health promotion programmes • e-health Guidelines and protocols • E-learning and education in healtcare • Telemedicine Software, Portals, Devices and Telehealth. • Public health and consumer informatics • Data Mining and Knowledge Discovery in Medicine. • ICT for Patient empowerment • ICT for Patient safety • Medical Databanks, Databases, and Knowledge Bases. • Neurocomputing in Medicine. • Healthcare Quality assurance • Nursing Informatics • Evaluation and technology assessment • Home-based eHealth • Health Management Issues • Health Research • Health Economics Issues Paper Submission Authors are invited to submit papers for this journal through E-mail: [email protected] . Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/hiij/index.html
Views: 27 hiij journal4
Data Mining For Thesis
 
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-- Created using PowToon -- Free sign up at http://www.powtoon.com/join -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 674 Suyeon Jung
"How machine learning helps cancer research" by Evelina Gabasova
 
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Machine learning methods are being applied in many different areas - from analyzing financial stock markets to movie recommender engines. But the same methods can be applied to other areas that also deal with big messy data. In bioinformatics I use similar machine learning, only this time to help find the underlying mechanisms of cancer. The problems in bioinformatics might seem opaque and confusing - sequencing, DNA, methylation, ChIP-seq, motifs etc. But underneath, the same algorithms that are used to find groups of customers based on their buying behavior can be used to find subtypes of cancer that respond differently to treatments. Algorithms for text analysis can be used to find important patterns in DNA strands. And software verification tools can help analyze biological systems. In this talk, I'll show you the exciting world of machine learning applications in bioinformatics. No knowledge of biology is required, the talk will be mostly in developer-speak. Evelina Gabasova UNIVERSITY OF CAMBRIDGE @evelgab Evelina is a machine learning researcher working in bioinformatics, trying to reverse-engineer cancer at University of Cambridge. Her background is mainly in computer science, statistics and machine learning. Evelina is a big fan of F# and uses it frequently for data manipulation and exploratory analysis in her research. Outside of academia, she also speaks at developer conferences and user groups about using F# for data science. She writes a blog at http://www.evelinag.com.
Views: 10428 Strange Loop
The best stats you've ever seen | Hans Rosling
 
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http://www.ted.com With the drama and urgency of a sportscaster, statistics guru Hans Rosling uses an amazing new presentation tool, Gapminder, to present data that debunks several myths about world development. Rosling is professor of international health at Sweden's Karolinska Institute, and founder of Gapminder, a nonprofit that brings vital global data to life. (Recorded February 2006 in Monterey, CA.) TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes. TED stands for Technology, Entertainment, Design, and TEDTalks cover these topics as well as science, business, development and the arts. Closed captions and translated subtitles in a variety of languages are now available on TED.com, at http://www.ted.com/translate. Follow us on Twitter http://www.twitter.com/tednews Checkout our Facebook page for TED exclusives https://www.facebook.com/TED
Views: 2912939 TED
Text Mining in Publishing
 
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TEXT MINING AND SCHOLARLY PUBLISHING: This short video by John Bond of Riverwinds Consulting discusses Text Mining and the Scholarly Publishing Industry. MORE VIDEOS on TEXT MINING and Scholarly Publishing can be found at: https://www.youtube.com/playlist?list=PLqkE49N6nq3jY125di1g8UDADCMvCY1zk FIND OUT more about John Bond and his publishing consulting practice at www.RiverwindsConsulting.com SEND IDEAS for John to discuss on Publishing Defined. Email him at [email protected] or see http://www.PublishingDefined.com CONNECT Twitter: https://twitter.com/JohnHBond LinkedIn: https://www.linkedin.com/in/johnbondnj Google+: https://plus.google.com/u/0/113338584717955505192 Goodreads: https://www.goodreads.com/user/show/51052703-john-bond YouTube: https://www.youtube.com/c/JohnBond BOOKS by John Bond: The Story of You: http://www.booksbyjohnbond.com/the-story-of-you/about-the-book/ You Can Write and Publish a Book: http://www.booksbyjohnbond.com/you-can-write-and-publish-a-book/about-the-book/ TRANSCRIPT: Hi there. I am John Bond from Riverwinds Consulting and this is Publishing Defined. Today I am going to discuss text mining as it relates to scholarly publishing. Text mining also goes by the phrase text data mining or text analytics. Text mining in scholarly publishing is the process of deriving high-quality information from peer reviewed articles and other content. It does this by processing large amounts of information and looking for patterns within the data, and then evaluating and interpreting the results. Text mining is most beneficial to researchers or other power users of technical content. It is very different from a keyword search such that you might perform with Google. A key word search likely produces thousands of web links with no uniformity in the results and certainly no ability to draw meaningful conclusions. An example: let’s say you are researching bladder cancer in men and you are looking for specific biomarkers for other disease states. You probably don’t have the time to review all the literature you might find through a search at PubMed. Text mining will review the available literature. It understands the parts of speech (nouns, verbs), recognizes abbreviations, takes term frequency into account, and other natural language processes. It will filter through all the content, extracts relevant facts, spot patterns, and provides the researcher with a more condensed set of results and statements than a literature search or a cursory review of abstracts ever could. It knows bladder cancer is a disease state. It knows, in this instance, to look for men as opposed to women. It understands what a biomarker is and how to apply this term to other disease states. It understands bladder cancer is a phrase and not being used as two separate terms. Text mining software involves high level programming and such concepts as word frequency distribution, pattern recognition, information extraction, and natural language processing as well as other programming concepts well beyond the scope of this video. The overall goal is to turn text into data for analysis and thereby help to draw conclusions. However, the results of text mining in and of themselves is not the end product, just part of the process. Individual text mining tools or enterprise level ones have become more common with researchers, librarians, and large for profit and not for profit organizations, and they will only grow. Aside from a text mining tool, an application is also necessary to check that the content being mined is licensed and to provide appropriate links to the content. Text mining is important to publishers or any group that holds large stores of full text articles or databases because this information as a whole has greater value than each individual part. Text mining can help extract that value. A key point for publishers is that the text mining tool and its user, such as a researcher, needs to have access to the content either by it being open access, through a subscription, or through a purchase. Subscription publishers see revenue when content is accessed or purchased. All publishers see article downloads and page views from text mining efforts. Either way, text mining as a tool in research, in medicine, in pharmaceutical R&D will only continue to grow in importance. Well that’s it. Please subscribe to my YouTube channel or click on the playlist to see more videos about text mining in scholarly publishing. And make comments below or email me with questions. Thank so much and take care.
Views: 313 John Bond
A Systematic Review on Educational Data Mining | Final year Projects 2016 - 2017
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 534 Clickmyproject
Biomedical Big Data Revolution | Dr. Stefan Bekiranov | TEDxRVA
 
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Find a cure for cancer from the comfort of your living room while in your PJs. It’s more possible today than it was a short time ago. We are currently undergoing a revolution in the field of biomedical research that will enable tailoring preventative strategies and therapies directly for each patient--Precision medicine. Systems Biologist, Stefan Bekiranov talks about what’s driving this revolution and how researchers are finding potential cures to diseases such as cancer at a faster rate than ever before. This talk was given at a local TEDx event, produced independently of the TED Conferences. It was filmed and edited by Tijo Media at the Carpenter Theatre at Dominion Arts Center in Richmond, VA. #medicalresearch #UVA #biomedical #bigdata #cancer #research #medicine After receiving his Bachelor of Science in Electrical Engineering from UCLA, Dr. Stefan Bekiranov worked as a microwave engineer at Raytheon Electromagnetic Systems Division in Santa Barbara. He received his PhD in theoretical condensed matter physics from the University of California at Santa Barbara and went on to do postdoctoral research in statistical/condensed matter physics at the University of Maryland. After that, Dr. Bekiranov conducted more postdoctoral research in computational biology at The Rockefeller University. He pioneered the analysis of high-resolution genomic tiling array data as a Bioinformatics Staff Scientist at Affymetrix. He is now an Associate Professor at the University of Virginia School of Medicine working in the fields of epigenomics and systems biology and has published over 50 papers in peer-reviewed journals. The ultimate goal of his work is to arrive at improved therapeutic targets to treat and hopefully, one day, cure cancer. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 17705 TEDx Talks
Mining Human Activity Patterns from Smart Home Big Data for Healthcare Applications
 
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Mining Human Activity Patterns from Smart Home Big Data for Healthcare Applications To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org Nowadays, there is an ever-increasing migration of people to urban areas. Health care services is one of the most challenging aspects that is greatly affected by the vast influx of people to city centers. Consequently, cities around the world are investing heavily in digital transformation in an effort to provide healthier ecosystem for people. In such transformation, millions of homes are being equipped with smart devices (e.g. smart meters, sensors etc.) which generate massive volumes of fine-grained and indexical data that can be analyzed to support smart city services. In this paper, we propose a model that utilizes smart home big data as a means of learning and discovering human activity patterns for health care applications. We propose the use of frequent pattern mining, cluster analysis and prediction to measure and analyze energy usage changes sparked by occupants’ behavior. Since people’s habits are mostly identified by everyday routines, discovering these routines allows us to recognize anomalous activities that may indicate people’s difficulties in taking care for themselves, such as not preparing food or not using shower/bath. Our work addresses the need to analyze temporal energy consumption patterns at the appliance level, which is directly related to human activities. For the evaluation of the proposed mechanism, this research uses the UK Domestic Appliance Level Electricity dataset (UK-Dale) - time series data of power consumption collected from 2012 to 2015 with time resolution of six seconds for five houses with 109 appliances from Southern England. The data from smart meters are recursively mined in the quantum/data slice of 24 hours, and the results are maintained across successive mining exercises. The results of identifying human activity patterns from appliance usage are presented in details in this paper along with accuracy of short and long term predictions.
Views: 621 JPINFOTECH PROJECTS
CardioAI: Automated Medical Diagnosis from MRI and Patient Data Using Deep Learning with Ivo Everts
 
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In order to alleviate medical staff from time consuming manual labour, GoDataDriven and the cardiovascular department of the University Medical Center Groningen teamed up, to apply deep learning to MRI data for automated inpainting of the left- and right- heart chambers and the heart muscle. The deformation of the heart regions during a heart beat is indicative for deciding for a pacemaker, or assessing the outcome of medicine usage. Automating the image segmentation task and the extraction of characteristic metrics of the heart function would therefore be extremely useful. We have implemented the deep U-net model that is used for image segmentation in Python and Keras, and deployed this in an API on Google Cloud Platform for large-scale usage. This work is part of a new ongoing initiative from GoDataDriven called 'Medicx.ai,' with which we strive to accelerate the data-driven way of working in the healthcare industry.
Views: 331 Databricks
Identifying product opportunities using social media mining: Application of topic modeling
 
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Identifying product opportunities using social media mining: Application of topic modeling and chance discovery theory - IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project BIG DATA 1. A Meta Path based Method for Entity Set Expansion in Knowledge Graph 2. Towards Green Cloud Computing: Demand Allocation and Pricing Policies for Cloud Service Brokerage 3. Security-Aware Resource Allocation for Mobile Social Big Data: A Matching Coalitional Game Solution 4. Revocable Identity-Based Access Control for Big Data with Verifiable Outsourced Computing 5. An Efficient and Fine-Grained Big Data Access Control Scheme With Privacy-Preserving Policy 6. HDM:A Compostable Framework for Big Data Processing 7. Dip-SVM : Distribution Preserving KernelSupport Vector Machine for Big Data 8. A Secure and Verifiable Access Control Scheme for Big Data Storage in Cloud 9. Game Theory Based Correlated Privacy Preserving Analysis in Big Data 10. Secure Authentication in Cloud Big Data with Hierarchical Attribute Authorization Structure 11. System to Recommend the Best Place to Live Based on Wellness State of the User Employing 12. Efficient Top-k Dominating Computation on Massive Data 13. Big Data Based Security Analytics for Protecting Virtualized Infrastructures in Cloud Computing 14. Disease Prediction by Machine Learning over Big Data from Healthcare Communities 15. Machine Learning with Big Data: Challenges and Approaches 16. Analyzing Healthcare Big Data with Predictionfor Future Health Condition 17. Robust Big Data Analytics for Electricity Price Forecasting in the Smart Grid 18. iShuffle: Improving Hadoop Performance with Shuffle-on-Write 19. Optimizing Share Size in Efficient and Robust Secret Sharing Scheme 20. Big data privacy in Biomedical research 21. Mining Human Activity Patterns From Smart Home Big Data for Health Care Applications 22. STaRS: Simulating Taxi Ride Sharing at Scale 23. Modeling Urban Behavior by Mining Geotagged Social Data 24. Apriori Versions Based on MapReduce for Mining Frequent Patterns on Big Data 25. Managing Big data using Hadoop Map Reduce in Telecom Domain 26. A Security Model for Preserving the Privacy of Medical Big Data in a Healthcare Cloud Using a Fog Computing Facility with Pairing-Based Cryptography 27. Mutual Privacy Preservingk-Means Clustering in Social Participatory Sensing 28. Measuring Scale-Up and Scale-Out Hadoop with Remote and Local File Systems and Selecting the Best Platform 29. Efficient Recommendation of De-identification Policies using MapReduce CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS 1. RRPhish Anti-Phishing via Mining Brand Resources Request 2. Confidence-interval Fuzzy Model-based Indoor Localization COMPUTER-BASED MEDICAL SYSTEMS (CBMS) 1. Population Health Management exploiting Machine Learning Algorithms to identify High-Risk Patients (23 July 2018) PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1. Trunk-Branch Ensemble Convolutional Neural Networks for Video-based Face Recognition ( April 1 2018 ) 2. Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection 3. Ordinal Constraint Binary Coding for Approximate Nearest Neighbor Search
Bottos Project Review - AI Data Mining on Blockchain - 2018 GEM!
 
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Bottos Project Review - AI Data Mining on Blockchain - 2018 GEM! Please like & subscribe to my Social Networks :) YouTube Channel: Click subscribe above! Twitter: https://twitter.com/NYCryptoTalk Steem: https://steemit.com/@nycryptotalk 💲 Buy Bitcoin (Free $10 with $100 or more initial purchase) 💲 https://www.coinbase.com/join/542d451... 💱 My favorite Exchanges - Kucoin & Binance 💱 https://www.kucoin.com/#/?r=1Kuf4 https://www.binance.com/?ref=11679736 https://www.qryptos.com 🏦 My favorite Crypto Wallet - The secure Ledger Nano S 🏦 https://goo.gl/dZL5Ui 💖 Donations (Message me for Shoutouts!) 💖 💎Bitcoin : 33LXztUAsDiEau4Dhq2EHvqA1aZDCrgTcR 💎NEO: ANwjKCEN2f2pDqM6sg85TFQkuWVhnLLZVA 💎ETH: 0xD75fb3436b386Da14fC807d4623d6231AA3D0179 💎LTC : MPGvsqczrGf7v7e1a9ftXhEokW337spMEb ☁️ Crypto Taxes ☁️ Get Started here: https://bitcoin.tax/r/qGKCxwoH Disclaimer: I AM NOT A FINANCIAL ADVISOR. MY VIEWS ARE GENERAL IN NATURE AND SHOULD NOT BE TAKEN AS FINANCIAL ADVICE. ALWAYS DO YOUR OWN RESEARCH BEFORE INVESTING ANY MONEY.
Views: 748 NYCryptoTalk
Big Data (Introduction for Business Students)
 
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This short revision video introduces the concept of big data. Big data is the process of collecting and analysing large data sets from traditional and digital sources to identify trends and patterns that can be used in decision-making. These large data sets are both structured (e.g. sales transactions from an online store) and unstructured (e.g. posts) on social media. The quantity of data generated is growing exponentially, including data generated by: Retail e-commerce databases User-interactions with websites and mobile apps Usage of logistics, transportation systems, financial and health care Social media data Location data (e.g. GPS-generated) Internet of Things (IoT) data generated New forms of scientific data (e.g. human genome analysis) Some important uses of big data include: Tracking and monitoring the performance, safety and reliability of operational equipment (e.g. data generated by sensors) Generating marketing insights into the needs and wants of customers, based on the transactions, feedback, comments (e.g. from e-commerce analytics, social media posts). Big data is revolutionising traditional market research. Improved decision-making - for example analysing the real-time impact of pricing changes or other elements of the marketing mix (the use of big data to drive dynamic pricing is a great example of this). Better security of business systems: big data can be analysed to identify unusual activity, for example on secure-access systems More efficient management of capacity: the increasing use of big data to inform decision-making about capacity management (e.g, in transportation and logistics systems) is a great example of how big data can help a business operate more efficiently
Views: 3425 tutor2u
PARC Forum: "Machine Learning and Design Thinking for Personalized Healthcare"
 
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PARC Forum Presents: Personalized healthcare and precision medicine are introducing novel opportunities to leverage data about an individual to deliver personalized health profiles and wellness profiles to an individual. This data includes electronic medical records, genomics, lifestyle, and environmental data. However, there are fundamental challenges from collecting such data at an individual level to integrating it to developing machine learning algorithms and tools to deliver on the promise and outcome of personalized healthcare. Our research program is focused on these challenges. I will present some of our research on developing personalized disease risk profiles from electronic medical records (EMR) and bringing together the spectrum of EMR to lifestyle data to guide a patient-centered population health management framework. Nitesh Chawla is the Frank M. Freimann Professor of Computer Science and Engineering, and Director of the research center on network and data sciences (iCeNSA) at the University of Notre Dame. He started his tenure-track career at Notre Dame in 2007, and quickly advanced from assistant professor to endowed full professor position in 2015. He has brought in over $19M dollars in research funding to the university since 2007, and has published over 180 papers. His research program has received a number of best paper awards and also frequent press coverage. He is the recipient of the IEEE CIS Outstanding Early Career Award; the IBM Watson Faculty Award, the IBM Big Data and Analytics Faculty Award, National Academy of Engineering New Faculty Fellowship, and Outstanding Dissertation Award. In recognition of the societal and community driven impact of his research, he was recognized with the Rodney Ganey Award and Michiana 40 Under 40. He is a two-time recipient of Outstanding Teaching Award at Notre Dame. He is a frequent speaker at national and international venues. He is a Fellow of the Reilly Center for Science, Technology, and Values; Fellow of the Institute of Asia and Asian Studies; and Fellow of the Kroc Institute for International Peace Studies Notre Dame. He founder of the data science company, Aunalytics, Inc. This event took place on August 18, 2016.
Data Science in 25 Minutes with GP Pulipaka (Ganapathi Pulipaka): Mastering TensorFlow Tutorial
 
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Ganapathi Pulipaka Chief Data Scientist for AI strategy, neural network architectures, application development of Machine learning, Deep Learning algorithms, experience in applying algorithms, integrating IoT platforms, Python, PyTorch, R, JavaScript, Go Lang, and TensorFlow, Big Data, IaaS, IoT, Data Science, Blockchain, Apache Hadoop, Apache Kafka, Apache Spark, Apache Storm, Apache Flink, SQL, NoSQL, Mathematics, Data Mining, Statistical Framework, SIEM with 6+ Years of AI Research and Development Experience in AWS, Azure, and GCP. Education: PostDoc– CS, PhD in Machine Learning, AI, Big Data Analytics, Engineering and CS, Colorado Technical University, Colorado Springs PhD, Business Administration in Data Analytics, Management Information Systems and Enterprise Resource Management, California University, Irvine Design, develop, and deploy machine learning and deep learning applications to solve the real-world problems in natural language processing, speech recognition, text to speech, chatbots, and speech to text analytics. Experience in data exploration, data preparation, applying supervised and unsupervised machine learning algorithms, machine learning model training, machine learning model evaluation, predictive analytics, bio-inspired algorithms, genetic algorithms, and natural language processing. I wrote around 400 research papers, published two books as a bestselling author on Amazon "The Future of Data Science and Parallel Computing," "Big Data Appliances for In-Memory Computing: A Real-World Research Guide for Corporations to Tame and Wrangle Their Data," and with a vast number of big data tool installations, SQL, NoSQL, practical machine learning project implementations, data analytics implementations, applied mathematics and statistics for publishing with the Universities as part of academic research programs. Currently, I’m working a video course “Mastering PyTorch for Advanced Data Scientist,” to build millions of data scientists around the world for AI practice. I implemented Many projects for Fortune 100 corporations Aerospace, manufacturing, IS-AFS (Apparel footwear solutions), IS-MEDIA (Media and Entertainment), ISUCCS (Customer care services), IS-AUTOMOTIVE (Automotive), IS-Utilities, retail, high-tech, life sciences, healthcare, chemical industry, banking, and service management. Public Keynote Speaker on Robotics and artificial intelligence held on May 21-22 at Los Angeles, CA. Published eBook in November 2017 for SAP Leonardo IoT “The Digital Evolution of Supply Chain Management with SAP Leonardo,” sponsored by SAP. Published eBook in December 2017 for Change HealthCare (McKesson’s HealthCare Corporation) on Machine Learning and Artificial Intelligence for Enterprise HealthCare and Health. Building recommendation systems and applying algorithms for anomaly detection in the financial industry. Deep reinforcement learning algorithms for robotics and IoT. Applying convolutional neural networks, recurrent neural networks, and long-term short memory with deep learning techniques to solve various conundrums. Developed number of machine learning and deep learning programs applying various algorithms and published articles with architecture and practical project implementations on GitHub, medium.com, data driven investor Experience with Python, TensorFlow, Caffe, Theano, Keras, Java, and R Programming languages implementing stacked auto encoders, backpropagation, perceptron, Restricted Boltzmann machines, and Deep Belief Networks. Experience in multiple IoT platforms. Twitter: https://twitter.com/gp_pulipaka Facebook: https://www.facebook.com/ganapathipulipaka LinkedIn: https://www.linkedin.com/in/dr-ganapathi-pulipaka-56417a2
Views: 164 GP Pulipaka