Vibha Anand photoPeter Bak photo Marion J. Ball photo Roy J. Byrd photoAmos Cahan photoHung-Yang  (Henry) Chang photo
Ching-Hua Chen photo Yu Cheng photo James V. Codella photoCatalina Danis photo Subhro Das photoÇağatay Demiralp photo
 Sanjoy Dey photo Soumya Ghosh photo PEI-YUN S. (SABRINA) HSUEH photo photo Joseph Jasinski photo photo
 Eileen Koski photo photo Ying Li photo photo Bin Liu photoHeng Luo photo
 Chandramouli Maduri photo photo photoAdam Perer photoHarry Stavropoulos photo photo
 Janu Verma photoPing Zhang photoXINXIN ZHU photo

Health Care Analytics

Research, development and application of data mining and machine learning techniques to analyze, model and derive insights from real world healthcare data.

Computational Health Behavior & Decision Science

Computational methods to recognize behaviors from data, compare alternative intervention programs and deliver data-driven insights to individuals in a way that motivates and supports behavior change

Group Name

Center for Computational Health


Research at the Interface of Data Science and Health

 

We pursue research in the application of data science to healthcare across the entire continuum from the health of individuals, to that of populations, to the healthcare system itself.

Healthcare is in the midst of dramatic changes on many levels, driven in no small part by the expanding role of data in achieving a deeper understanding of disease, behavior and the interaction of complex systems. New types of data, such as genomic and sensor data, combined with the increasing electronic availability of traditional health data, are having a major impact on conceptual models of how disease is diagnosed and treated.

The Center for Computational Health at IBM T.J. Watson Research Center consists of a multi-disciplinary team of researchers with expertise in machine learning, data mining, visual analytics, biomedical & medical informatics, statistics, behavioral and decision sciences, and medicine. We work on developing cutting-edge methodologies to derive insights from diverse sources of health data, to support use cases in personalized care delivery and management, real world evidence, health behavior modeling, cognitive health decision support, and translational informatics.

Program Director: Jianying Hu

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Research Areas

 

Patient Similarity Analytics

Incorporating diverse patient attributes to develop similarity analytics by applying advanced machine learning methods to identify precision cohorts, combined with modeling methodologies for personalized predictive models capable of identifying patient level rankings of risk factors, leading to more targeted and actionable insights.

 

Predictive Modeling

Advanced machine learning approaches to address challenges in developing effective and efficient predictive models from observational healthcare data in different use cases. Examples include matrix based methods to address sparsity, feature engineering (i.e., temporal pattern mining, factor analysis), feature selection, scalable predictive modeling platform, personalized predictive modeling leveraging precision cohorts, and multi-task learning for comprehensive risk assessment.

 

Disease Progression Modeling

Understanding disease onset, characteristics of disease stages, rate of progression from asymptomatic to symptomatic disease, from earlier to more severe stages, and factors that influence disease progression pathways.  

 

Translational Informatics

Drug Similarity Analytics combined with advanced machine learning methods such as joint matrix factorization can help pharmaceutical researchers quickly identify drugs that have similar characteristics to target drugs, supporting three distinct, but equally important use-cases: Drug Safety, Drug Repositioning and Personalized Medicine.

 

Visual Analytics and Cognitive Decision Support

Innovative visual analytics platform and user interfaces that accelerate the process of exploring and mining data to derive new insights that can be translated into more effective therapeutics and processes.

 

Contextual & Behavioral Modeling

Combining real-time data from wearable devices, self-reported activity and clinical data, allows us to model behavior for both prediction and personalized wellness and fitness strategies customized to an individual’s unique needs.

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Recent Presentations & Events

 

European Semantic Web Conference (ESWC), 5/29-6/2/16, Anissaras, Crete, Greece

Best In-Use/Industrial PaperAward - Predicting Drug-Drug Interactions through Large-scale Similarity-Based Link Prediction

Authors: Achille Fokoue, Mohammad Sadoghi, Oktie Hassanzadeh, and Ping Zhang 

http://2016.eswc-conferences.org/awards-and-closing

 

IBM Watson Health Showcases Progress Tackling Diabetes at American Diabetes Association’s 76th Scientific Sessions, June 10-14, 2016, New Orleans, LA

Personalized predictive modeling work led by Kenny Ng featured in the press release:

http://www-03.ibm.com/press/us/en/pressrelease/49904.wss

 

2016 SIAM International Conference on Data Mining, May 5-7, 2016, Miami, FL

Tutorial Presentation: Biomedical Data Mining with Matrix Models

Presenter: Ping Zhang

http://www.siam.org/meetings/sdm16/tutorials.php

 

6th International Conference on Digital Health, April 11-13, 2016, Montreal, Quebec, Canada

Keynote Presentation - "Health Innovation - An IBM Perspective"

Presenter: Ching-Hua Chen

http://www.acm-digitalhealth.org

 

ENDO 2016, April 1-4, 2016, Boston, MA

Symposium: Advanced Healthcare Informatics Analytics in the Areas of Precision Medicine, Translational Medicine and Population Health

Presenters: Kenney Ng, Yarra Goldschmidt, Ching-Hua Chen

https://endo.confex.com/endo/2016endo/webprogram/Session7819.html

 

2016 Asian American Engineer of the Year Symposium, March 12, 2016, New Brunswick, NJ

Planary speach on Data Driven Healthcare Analytics

Planary Speaker: Jianying Hu

http://www.aaeoy.org/symposium.html

 

CHDI’s 11th Annual HD Therapeutics Conference, February 22–25, 2016, Palm Springs, CA

Invited closing presentation: Understanding Huntington’s disease progression: A multi–level probabilistic modeling approach

Presenter: Jianying Hu

http://chdifoundation.org/2016-conference

 

SINAInnovations 2015, October 27-28, 2015, New York, NY

Day One Panel Discussion - Precision Medicine

Presenter: Jianying Hu

Program & Video Link: http://icahn.mssm.edu/about/sinainnovations

 

Machine Learning in Healthcare, August 8-9, 2014, Los Angeles, CA

Keynote: Data Driven Analytics for Personalized Healthcare

Presenter: Jianying Hu

Program & Video Link: chttp://mucmd.org/conference-2014.html

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Selected Recent Publications

 

Visual assessment of the similarity between a patient and trial population: Is This Clinical Trial Applicable to My Patient?

 

Cahan A, Cimino JJ.

Applied Clinical Informatics, 2016 7 2: 477-488.

 

Risk Prediction with Electronic Health Records: A Deep Learning Approach

Cheng Y, Wang F, Zhang P, Hu J.

SIAM International Conference on Data Mining (SDM), 2016.

 

Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models

Krause J, Perer A, and Ng K.

Proceedings of the 2016 CHI Conference in Human Factors in Computing Systems, 2016

 

Wearable Technologies and Telehealth in Care Management for Chronic Illness.

Zhu, Xinxin, and Cahan, Amos.

In Healthcare Information Management Systems

Charlotte A. Weaver, Marion J. Ball, George R. Kim, and Joan M. Kiel, Eds, Springer International Publishing, 2016. 

 

Integrating Population-based Patterns with Personal Routine to Re-engage Fitbit Use.

Chung C, Danis C.

Proceedings of PervasiveHealth 2016, 2016

 

Mining and exploring care pathways from electronic medical records with visual analytics.

A. Perer, F. Wang, and J. Hu.

Journal of Biomedical Informatics (JBI). 2015

Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects.

Zhang P, Wang F, Hu J, Sorrentino R.

Sci Rep. 2015 Jul 21;5:12339.

 

Towards actionable risk stratification: a bilinear approach

Wang X, Wang F, Hu J., Sorrentino, R

Journal of Biomedical Informatics (JBI). 2015

 

Personalized Predictive Modeling and Risk Factor Identification using Patient Similarity

Ng K, Sun J, Hu J, Wang F

AMIA Jt Summits Transl Sci Proc. 2015 Mar 25;2015:132-6.

 

LINKAGE: An Approach for Comprehensive Risk Prediction for Care Management

Sun Z, Wang F, HU J

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2015

 

Early detection of heart failure with varying prediction windows by structured and unstructured data in electronic health records.

Yajuan Wang, Ng K, Byrd RJ, Jianying Hu, Ebadollahi S, Daar Z, deFilippi C, Steinhubl SR, Stewart WF.

Conf Proc IEEE Eng Med Biol Soc. 2015 Aug;2015:2530-3.

 

Clinicians' evaluation of computer-assisted medication summarization of electronic medical records.

Zhu X, Cimino JJ. 

Comput Biol Med. 2015 Apr;59:221-31.

 

Prescription Extraction from Clinical Notes: Towards Automating EMR Medication Reconciliation

Wang Y, Steinhubl SR, Defilippi C, Ng K, Ebadollahi S, Stewart WF, Byrd RJ.

AMIA Jt Summits Transl Sci Proc. 2015 Mar 25;2015:188-93.

 

Relative Patterns Discovery toward Big Data Analytics

Pai H, Wu F, Hsueh PY, Lin G, Chan Y-H.

Proceedings of the 2015 IEEE 12th Interntional Conference e-Business Engineering (ICEBE), 2015

 

PARAMO: a PARAllel predictive MOdeling platform for healthcare analytic research using electronic health records

Ng K, Ghoting A, Steinhubl SR, Stewart WF, Malin B, Sun J

Journal of Biomedical Informatics (JBI), 2014

 

Unsupervised Learning of Disease Progression Models

Wang X, Sontag D, Wang F

Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD), 2014

 

Predicting changes in hypertension control using electronic health records from a chronic disease management program

Sun J, McNaughton CD, Zhang P, Perer A, Gkoulalas-Divanis A, Denny JC, Kirby J, Lasko T, Salp A, Malin BA

Journal of American Medical Informatics Association (JAMIA), 2014

 

From micro to macro: data driven phenotyping by densification of longitudinal electronic medical records

Zhou J, Wang F, Hu J, Ye J

Proceedings of 0th ACM SIGKDD international conference on Knowledge discovery and data mining, Pages 135-144  (KDD), 2014

 

Towards personalized medicine: leveraging patient similarity and drug similarity analytics

Zhang P, Wang F, Hu J, Sorrentino R

Proceedings of AMIA Joint Summits on Translational Sciences, 2014

 

Exploring joint disease risk prediction

Wang X, Wang F, Hu J, Sorrentino R.

Proceeding of AMIA Annual Symposium, 2014

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