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.
Selected HEALS Publications
- Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph; Yu Chen, Ananya Subburathinam 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
- 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
- Reciptor: An Effective Pretrained Model for Recipe Representation Learning, Diya Li and Mohammed J. Zaki,
ACM Special Interest Group on Knowledge Discovery and Data Mining Conference(SIGKDD 2020)
- GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension; Yu Chen , Lingfei Wu and Mohammed J. Zaki; 29th International Joint Conference on Artificial Intelligence (IJCAI 2020), Yokohama, Japan, Jul 11-17, 2020.
For the full list of HEALS publications please see here.
Awards and Recognitions
- 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
Semantic Technology Resources
Knowledge Graph Frameworks
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.