Personalized Healthcare

A problem in healthcare is that in spite of the recent focus on precision medicine, much of the relevant data is not patient-specific, and thus, corroborating relevant information and discarding the rest remains the manual endeavor of clinicians. This is a rather complex problem, with several aspects to it such as laboratory tests, prescription drugs, diet, etc. We developed AI-driven systems that can distill patient-specific information from large amounts of natural language data as well as structured databases. This has led to automatic recommendation of the most relevant laboratory tests for a patient, depending on the precise circumstances [Banerjee et al. 2014], and personalized identification of adverse drug reactions and attribution of patient's symptoms to their drug regimen [Banerjee et al. 2015].

Research Group

This research was a collaboration with the Department of Emergency Medicine at the Stony Brook University School of Medicine.

• Ritwik Banerjee, Research Assistant Professor of Computer Science, Stony Brook University

• Yejin Choi, Professor of Computer Science, University of Washington

• I. V. Ramakrishnan, Professor of Computer Science, Stony Brook University

• Mark C. Henry, Professor and Chair Emeritus, Emergency Medicine, Stony Brook University

• Matthew Perciavalle, Clinical Pharmacist, Emergency Medicine, Stony Brook University

• Gaurav Piyush. M.S.

• Ameya Naik, M.S.

Publications

• Ritwik Banerjee, Yejin Choi, Gaurav Piyush, Ameya Naik, and I. V. Ramakrishnan. Automated Suggestion of Laboratory Tests for Identifying Likelihood of Adverse Drug Events. In 2014 IEEE International Conference on Healthcare Informatics (ICHI), pp. 170 - 175. IEEE, 2014. [ PDF ]

• Ritwik Banerjee, I. V. Ramakrishnan, Mark Henry, and Matthew Perciavalle. Patient-Centered Identification, Attribution, and Ranking of Adverse Drug Events. In 2015 International Conference on Healthcare Informatics, pp. 18 - 27. IEEE, 2015. DOI: 10.1109/ICHI.2015.8 [ PDF ]