activity
20152020
most citedA Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation

25 citations · 40 across the 4 of their papers we have counts for

collaborators

9 papers

cs.LG202015 cited

Developing Personalized Models of Blood Pressure Estimation from Wearable Sensors Data Using Minimally-trained Domain Adversarial Neural Networks

Lida Zhang, Nathan C. Hurley, Bassem Ibrahim +4

Blood pressure monitoring is an essential component of hypertension management and in the prediction of associated comorbidities. Blood pressure is a dynamic vital sign with freque…

cs.LG2020

Learning to Ask Medical Questions using Reinforcement Learning

Uri Shaham, Tom Zahavy, Cesar Caraballo +3

We propose a novel reinforcement learning-based approach for adaptive and iterative feature selection. Given a masked vector of input features, a reinforcement learning agent itera…

cs.CL201925 cited

A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation

Irene Li, Michihiro Yasunaga, Muhammed Yavuz Nuzumlalı +4

Automated analysis of clinical notes is attracting increasing attention. However, there has not been much work on medical term abbreviation disambiguation. Such abbreviations are a…

eess.SP2019

A Survey of Challenges and Opportunities in Sensing and Analytics for Cardiovascular Disorders

Nathan C. Hurley, Erica S. Spatz, Harlan M. Krumholz +2

Cardiovascular disorders account for nearly 1 in 3 deaths in the United States. Care for these disorders are often determined during visits to acute care facilities, such as hospit…

cs.DC2018

A Scalable Data Science Platform for Healthcare and Precision Medicine Research

Jacob McPadden, Thomas JS Durant, Dustin R Bunch +9

Objective: To (1) demonstrate the implementation of a data science platform built on open-source technology within a large, academic healthcare system and (2) describe two computat…

cs.CV2018

Automated Characterization of Stenosis in Invasive Coronary Angiography Images with Convolutional Neural Networks

Benjamin Au, Uri Shaham, Sanket Dhruva +7

The determination of a coronary stenosis and its severity in current clinical workflow is typically accomplished manually via physician visual assessment (PVA) during invasive coro…