500 citations · 617 across the 11 of their papers we have counts for
5 papers · 1 filter
ERASER: A Benchmark to Evaluate Rationalized NLP Models
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani +4
State-of-the-art models in NLP are now predominantly based on deep neural networks that are opaque in terms of how they come to make predictions. This limitation has increased inte…
SUPP.AI: Finding Evidence for Supplement-Drug Interactions
Lucy Lu Wang, Oyvind Tafjord, Arman Cohan +5
Dietary supplements are used by a large portion of the population, but information on their pharmacologic interactions is incomplete. To address this challenge, we present SUPP.AI,…
Learning to Identify Patients at Risk of Uncontrolled Hypertension Using Electronic Health Records Data
Ramin Mohammadi, Sarthak Jain, Stephen Agboola +3
Hypertension is a major risk factor for stroke, cardiovascular disease, and end-stage renal disease, and its prevalence is expected to rise dramatically. Effective hypertension man…
An Analysis of Attention over Clinical Notes for Predictive Tasks
Sarthak Jain, Ramin Mohammadi, Byron C. Wallace
The shift to electronic medical records (EMRs) has engendered research into machine learning and natural language technologies to analyze patient records, and to predict from these…
Attention is not Explanation
Sarthak Jain, Byron C. Wallace
Attention mechanisms have seen wide adoption in neural NLP models. In addition to improving predictive performance, these are often touted as affording transparency: models equippe…