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HEALS

Health Empowerment by Analytics, Learning, and Semantics
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Supported by IBM

The primary goal of the HEALS (Health Empowerment by Analytics, Learning, and Semantics) project is to apply advanced cognitive computing capabilities to help people understand and improve their own health conditions. In particular, we are exploring areas including personalized and mobile medical care, improved healthcare analytics, and new data-based approaches to driving down the cost of medical care. Our main research thrusts are as follows:

  • Health - Including Personal Health Care and Precision Medicine
  • Empowerment by - Knowledge as Medicine for life scientists, translational researchers, clinicians and patients
  • Analytics - Using data for hypothesis formation and testing
  • Learning & - Both (continuous) machine-learning and human-in-loop improvement over time
  • Semantics - Integrating knowledge from many sources via probabilistic knowledge graph technology

RPI is an IBM AI Horizons Network member organization, and the HEALS project is a joint IBM-RPI effort with close collaboration and transition.

HEALS Team

Team

IBMRPI  

Faculty

 

Staff

PhD Students

 

Past Collaborators:

  • Megan Goulet
  • Steven Haussmann
  • Aaron Hill
  • Vincent Huang
  • Karthik Imayavaramban
  • Benjamin Kelly
  • Yuchen Liang
  • Dylan Le
  • Diya Li
  • Fengwei Liu
  • Runmin Lu
  • George Lu

 

Selected HEALS Publications

 

For the full list of HEALS publications please see here.

Awards and Recognitions

Machine Learning Resources

Semantic Technology Resources

Knowledge Graph Frameworks

Ontologies

Knowledge Graphs

Analytics

For more information about the RPI-IBM collaboration, please see https://science.rpi.edu/biology/news/ibm-and-rensselaer-team-research-chronic-diseases-cognitive-computing.

HEALS Internal Page

Project Gallery

HEALS Semantic Resources for CDS

HEALS Semantic Technology Resources for Clinical Decision Support Recommendations

Resources for Personal Health Applications

HEALS Resources for Personal Health Knowledge Graph Construction