Center for Computational Health - overview
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 Research 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
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.
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.
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.
Recent News and Posts
11/17 - New York Academy of Sciences Highlights the work of CCH Researcher Subhro Das:
10/25/17 - IBM Researchers publish article in PLOS ONE about MELD-Plus - A generalizable risk score for cirrhosis:
4/7/17 - IBM grantedU.S. Patent 9,536,194: Method and system for exploring the associations between drug side-effects and therapeutic indications.IBM press release:http://www-03.ibm.com/press/us/en/pressrelease/52017.wss Blog Post: https://www.ibm.com/blogs/research/2017/04/machine-learning-models-drug-discovery/ Video: https://www.youtube.com/watch?v=e3USliqAC9Q&feature=youtu.bePress: http://healthitanalytics.com/news/ibm-patents-machine-learning-model-for-pharmaceutical-discoveryhttps://finance.yahoo.com/news/ibm-patents-machine-learning-models-130000644.html
Articles of interest related to CHF prediction work recently published in Circulation: Cardiovascular Quality and Outcomes:IEEE Spectrum Article:http://spectrum.ieee.org/the-human-os/biomedical/diagnostics/ibm-intel-stanford-bet-on-ai-to-speed-up-disease-diagnosis-and-drug-discoveryBlog Post:https://www.ibm.com/blogs/research/2017/04/using-ai-to-predict-heart-failure/
Recent Presentations & Events
AMIA 2017 - 11/3-8/2017, Washington, D.C.
Distinguished Paper Nomination:
StressHacker: Towards Practical Stress Monitoring in the Wild with Smartwatches.
Authors: Tian Hao, Kimberly Walter, Marion Ball, Hung-yang Chang, Si Sun, XinXin Zhu
Keynote: IEEE ICHI 2017 - 8/23-26/2017, Park City, Utah
Keynote: Computational Methods for Next Generation Health Care
Presenter: Jianying Hu
Keynote: 7th Digital Health Conference 2017 - 7/2-5/2017, London England
Keynote: Health Innovation – An IBM Perspective
Presenter: Ching-Hua Chen
Submitters: Janu Verma, Bum Chul Kwon, Yu Cheng, Soumya Ghosh, Kenney Ng
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
Personalized predictive modeling work led by Kenny Ng featured in the press release:
Keynote Presentation - "Health Innovation - An IBM Perspective"
Presenter: Ching-Hua Chen
Symposium: Advanced Healthcare Informatics Analytics in the Areas of Precision Medicine, Translational Medicine and Population Health
Presenters: Kenney Ng, Yarra Goldschmidt, Ching-Hua Chen
Plenary speach on Data Driven Healthcare Analytics
Plenary Speaker: Jianying Hu
Invited closing presentation: Understanding Huntington’s disease progression: A multi–level probabilistic modeling approach
Presenter: Jianying Hu