Clinical Concept Embeddings Learned from Massive Sources of Medical Data. - Andrew L. Beam; Benjamin Kompa; Inbar Fried; Nathan P. Palmer; Xu Shi; Tianxi Cai; Isaac S. Kohane (CoRR, 2018)

Learning a Generative Model of Cancer Metastasis. - Benjamin Kompa; Beau Coker (CoRR, 2019)

Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data. - Andrew L. Beam; Benjamin Kompa; Allen Schmaltz; Inbar Fried; Griffin M. Weber; Nathan P. Palmer; Xu Shi; Tianxi Cai; Isaac S. Kohane (PSB, 2020)

Learning a Latent Space of Highly Multidimensional Cancer Data. - Benjamin Kompa; Beau Coker (PSB, 2020)

Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures. - Benjamin Kompa; Jasper Snoek; Andrew L. Beam (Entropy, 2021)

Second opinion needed: communicating uncertainty in medical machine learning. - Benjamin Kompa; Jasper Snoek; Andrew L. Beam (npj Digit. Medicine, 2021)

Deep Learning Methods for Proximal Inference via Maximum Moment Restriction. - Benjamin Kompa; David R. Bellamy; Thomas Kolokotrones; James M. Robins; Andrew Beam (NeurIPS, 2022)

Development and validation of a deep learning model for diagnosing neuropathic corneal pain via in vivo confocal microscopy. - Neslihan Dilruba Koseoglu; Eric Chen; Rudraksh Tuwani; Benjamin Kompa; Stephanie M. Cox; M. Cuneyt Ozmen; Mina Massaro-Giordano; Andrew L. Beam; Pedram Hamrah (npj Digit. Medicine, 2025)