HEALS

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.

Team

Past Collaborators:

Selected HEALS Publications

• Diya Li, Mohammed J. Zaki, Ching-hua Che; Health-guided recipe recommendation over knowledge graphs, 
  Journal of Web Semantics,2022,100743,ISSN 1570-8268,https://doi.org/10.1016/j.websem.2022.100743.
• Jonathan J. Harris and Mohammed J. Zaki. Towards neural numeric-to-text generation from temporal personal health data. In Workshop on Applied Data Science for Healthcare: Transparent and Human-centered AI (with KDD). Aug 2022.
• S Chari, P Chakraborty, M Ghalwash, O Seneviratne,  DM Gruen, FS Saiz, CH Chen, PM Rojas, DL McGuinness; “Leveraging Clinical Context for User-Centered Explainability: A Diabetes Use Case”; KDD Applied Data Science in Healthcare (DSHealth) Workshop; 2021 (Best Paper Award)
• A Framework for Generating Summaries from Temporal Personal Health Data; Jonathan Harris, Ching-Hua Chen, Mohammed J. Zaki; ACM Transactions on Computing for Healthcare
• Ingredient Substitutions Using a Knowledge Graph of Food; Sola S. Shirai, Oshani Seneviratne, Minor E. Gordon, Ching-Hua Chen, Deborah L. McGuinness;  Frontiers in Artificial Intelligence, section AI in Food, Agriculture and Water 
• Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph; Yu Chen, Ananya Subburathinam, Ching-Hua Chen, and Mohammed J. Zaki; The 14th ACM International Conference on Web Search and Data Mining 2021
• Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings; Yu Chen , Lingfei Wu and Mohammed J. Zaki; NeurIPS 2020
• Explanation Ontology: A Model of Explanations for User-Centered AI; Shruthi Chari , Oshani Seneviratne , Daniel M. Gruen ,  Morgan A. Foreman , Amar K. Das, Deborah L. McGuinness; 19th International Semantic Web Conference 2020 (Best Resource Paper Award)
• Knowledge Extraction of Cohort Characteristics in Research Publications, Jay D. S. Franklin , Shruthi Chari , Morgan A. Foreman , Oshani Seneviratne , Daniel M. Gruen , James P. McCusker , Amar K. Das , Deborah L. McGuinness; American Medical Informatics Association (AMIA) Annual Conference 2020

For the full list of HEALS publications please see here.

Awards and Recognitions

• Prof. Deborah McGuinness; Lifetime Achievement Award at the Knowledge Graph Conference 2022. Please see https://news.rpi.edu/approach/2022/06/09 for more details.
• Best Paper Award at the KDD Applied Data Science in Healthcare (DSHealth) Workshop; 2021 
  
• Best Student Paper Award at the Data Engineering Meets intelligent food and Cooking Recipes (DECOR) Workshop for Semantic Modeling for Food Recommendation Explanations; Ishita Padhiar, Oshani Seneviratne, Shruthi Chari, Dan Gruen, and Deborah McGuinness 
  
• Best Resource Paper Award at the 19th International Semantic Web Conference 2020 for Explanation Ontology: A Model of Explanations for User-Centered AI; Shruthi Chari, Oshani Seneviratne , Daniel M. Gruen ,  Morgan A. Foreman , Amar K. Das, Deborah L. McGuinness  
  
• Best student paper award at AAAI 2020 Workshop on Deep Learning on Graphs: Methodologies and Applications (AAAI DLGMA) 2020 for Deep Iterative and Adaptive Learning for Graph Neural Networks; Yu Chen , Lingfei Wu and Mohammed J. Zaki
• Best resource paper nomination at the 18th International Semantic Web Conference 2019 for Making Study Populations Visible through Knowledge Graphs; Shruthi Chari , Miao Qi, Nkecheniyere N. Agu, Oshani Seneviratne , James P. McCusker , Kristin P. Bennett, Amar K. Das, Deborah L. McGuinness
• Poster award (one of the top six) at the AI Research Week, September 16, 2019, hosted by MIT-IBM Watson AI Lab (award money: $1000) for Making Study Populations Visible through Knowledge Graphs; Shruthi Chari , Miao Qi, Nkecheniyere N. Agu, Oshani Seneviratne , James P. McCusker , Kristin P. Bennett, Amar K. Das, Deborah L. McGuinness

Machine Learning Resources

• Bi-directional attention memory Network
• GraphFlow
• Iterative Deep Graph Learning for Graph Neural Networks
• Personalized Food Recommendations
• Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation
• Reciptor
• Temporal Summaries

Semantic Technology Resources

Knowledge Graph Frameworks

• Tetherless World Knowledge Store
• Whyis

Ontologies

• Breast Cancer Staging
• Explanation
• Guideline Provenance
• Study Cohort
• SPARQL Query Agent-based Reasoning Engine

Knowledge Graphs

• Breast Cancer
• Food
• Semantics for Personal Health

Analytics

• Equity Browser

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