12 citations · 12 across the 1 of their papers we have counts for
5 papers
Large Language Models with Retrieval-Augmented Generation for Zero-Shot Disease Phenotyping
Will E. Thompson, David M. Vidmar, Jessica K. De Freitas +9
Identifying disease phenotypes from electronic health records (EHRs) is critical for numerous secondary uses. Manually encoding physician knowledge into rules is particularly chall…
Deep neural networks can predict mortality from 12-lead electrocardiogram voltage data
Sushravya Raghunath, Alvaro E. Ulloa Cerna, Linyuan Jing +12
The electrocardiogram (ECG) is a widely-used medical test, typically consisting of 12 voltage versus time traces collected from surface recordings over the heart. Here we hypothesi…
A Large-scale Multimodal Study for Predicting Mortality Risk Using Minimal and Low Parameter Models and Separable Risk Assessment
Alvaro E. Ulloa Cerna, Marios Pattichis, David P. vanMaanen +5
The majority of biomedical studies use limited datasets that may not generalize over large heterogeneous datasets that have been collected over several decades. The current paper d…
A deep neural network to enhance prediction of 1-year mortality using echocardiographic videos of the heart
Alvaro Ulloa, Linyuan Jing, Christopher W Good +13
Predicting future clinical events helps physicians guide appropriate intervention. Machine learning has tremendous promise to assist physicians with predictions based on the discov…
An Unsupervised Homogenization Pipeline for Clustering Similar Patients using Electronic Health Record Data
Alvaro Ulloa, Anna Basile, Gregory J. Wehner +5
Electronic health records (EHR) contain a large variety of information on the clinical history of patients such as vital signs, demographics, diagnostic codes and imaging data. The…