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Bethany Percha
Researcher at Icahn School of Medicine at Mount Sinai
Publications - 42
Citations - 1582
Bethany Percha is an academic researcher from Icahn School of Medicine at Mount Sinai. The author has contributed to research in topics: Population & Deep learning. The author has an hindex of 17, co-authored 41 publications receiving 1115 citations. Previous affiliations of Bethany Percha include University of Michigan & Mount Sinai Hospital.
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Journal ArticleDOI
Deep learning predicts hip fracture using confounding patient and healthcare variables
Marcus A. Badgeley,John R. Zech,Luke Oakden-Rayner,Benjamin S. Glicksberg,Manway Liu,William Gale,Michael V. McConnell,Bethany Percha,Thomas M. Snyder,Joel T. Dudley +9 more
TL;DR: In this paper, a single model that directly combines image features, patient and hospital process data outperforms a Naive Bayes ensemble of an image-only model prediction, patient, and hospital processes data.
Journal ArticleDOI
Informatics confronts drug-drug interactions.
Bethany Percha,Russ B. Altman +1 more
TL;DR: Recent developments that encompass a range of informatics approaches in this domain are reviewed, from the construction of databases for efficient searching of known DDIs to the prediction of novel DDIs based on data from electronic medical records, adverse event reports, scientific abstracts, and other sources.
Journal ArticleDOI
Transition from local to global phase synchrony in small world neural network and its possible implications for epilepsy.
TL;DR: A potential mechanism for the transition to pathological coherence underlying seizure generation is shown and it is shown that properties of phase synchronization in a two-dimensional lattice of nonidentical coupled Hindmarsh-Rose neurons change radically depending on the connectivity structure of the network.
Journal ArticleDOI
Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation.
Akhil Vaid,Sulaiman Somani,Adam Russak,Jessica K De Freitas,Fayzan Chaudhry,Ishan Paranjpe,Kipp W. Johnson,Samuel J. Lee,Riccardo Miotto,Felix Richter,Shan Zhao,Noam D. Beckmann,Nidhi Naik,Arash Kia,Prem Timsina,Anuradha Lala,Manish Paranjpe,Eddye Golden,Matteo Danieletto,Manbir Singh,Dara Meyer,Paul F. O'Reilly,Laura M. Huckins,Patricia Kovatch,Joseph Finkelstein,Robert Freeman,Edgar Argulian,Andrew Kasarskis,Bethany Percha,Judith A. Aberg,Emilia Bagiella,Carol R. Horowitz,Barbara Murphy,Eric J. Nestler,Eric E. Schadt,Judy H. Cho,Carlos Cordon-Cardo,Valentin Fuster,Dennis S. Charney,David Reich,Erwin P. Bottinger,Erwin P. Bottinger,Matthew A. Levin,Jagat Narula,Zahi A. Fayad,Allan C. Just,Alexander W. Charney,Girish N. Nadkarni,Benjamin S. Glicksberg +48 more
TL;DR: Externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons were developed and established model interpretability to identify and rank variables that drive model predictions.
Proceedings ArticleDOI
Discovery and explanation of drug-drug interactions via text mining
TL;DR: This work trains a random forest classifier to score potential DDIs based on the features of the normalized assertions extracted from the literature that relate two drugs to a gene product, and shows how the classifier can be used to explain known DDIs and to uncover new DDIs that have not yet been reported.