Hybrid Bayesian-rank integration approach improves the predictive power of genomic dataset aggregation. - Marcus A. Badgeley; Stuart C. Sealfon; Maria D. Chikina (Bioinform., 2015)

Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks. - Benjamin S. Glicksberg; Li Li 062; Marcus A. Badgeley; Khader Shameer; Roman Kosoy; Noam D. Beckmann; Nam Pho; Jörg Hakenberg; Meng Ma; Kristin L. Ayers; Gabriel E. Hoffman; Shuyu Dan Li; et al. (Bioinform., 2016)

Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams. - Khader Shameer; Marcus A. Badgeley; Riccardo Miotto; Benjamin S. Glicksberg; Joseph W. Morgan; Joel T. Dudley (Briefings Bioinform., 2017)

Wide and deep volumetric residual networks for volumetric image classification. - Varun Arvind; Anthony B. Costa; Marcus A. Badgeley; Samuel K. Cho; Eric K. Oermann (CoRR, 2017)

Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning. - Khader Shameer; Benjamin S. Glicksberg; Rachel Hodos; Kipp W. Johnson; Marcus A. Badgeley; Ben Readhead; Max S. Tomlinson; Timothy O'Connor; Riccardo Miotto; Brian A. Kidd; Rong Chen 006; Avi Ma'ayan; et al. (Briefings Bioinform., 2018)

Confounding variables can degrade generalization performance of radiological deep learning models. - John R. Zech; Marcus A. Badgeley; Manway Liu; Anthony B. Costa; Joseph J. Titano; Eric K. Oermann (CoRR, 2018)

CANDI: an R package and Shiny app for annotating radiographs and evaluating computer-aided diagnosis. - Marcus A. Badgeley; Manway Liu; Benjamin S. Glicksberg; Mark M. Shervey; John R. Zech; Khader Shameer; Joseph Lehar; Eric K. Oermann; Michael V. McConnell; Thomas M. Snyder; Joel T. Dudley (Bioinform., 2019)

PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model. - Benjamin S. Glicksberg; Boris Oskotsky; Phyllis M. Thangaraj; Nicholas Giangreco; Marcus A. Badgeley; Kipp W. Johnson; Debajyoti Datta; Vivek A. Rudrapatna; Nadav Rappoport; Mark M. Shervey; Riccardo Miotto; Theodore C. Goldstein; et al. (Bioinform., 2019)

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 (npj Digit. Medicine, 2019)

Controlling Level of Unconsciousness by Titrating Propofol with Deep Reinforcement Learning. - Gabriel Schamberg; Marcus A. Badgeley; Emery N. Brown (AIME, 2020)

Heterogeneous Graph Embeddings of Electronic Health Records Improve Critical Care Disease Predictions. - Tingyi Wanyan; Martin Kang; Marcus A. Badgeley; Kipp W. Johnson; Jessica K. De Freitas; Fayzan F. Chaudhry; Akhil Vaid; Shan Zhao; Riccardo Miotto; Girish N. Nadkarni; Fei Wang 001; Justin F. Rousseau; et al. (AIME, 2020)

Quantification of BERT Diagnosis Generalizability Across Medical Specialties Using Semantic Dataset Distance. - Mihir P. Khambete; William Su; Juan Garcia; Joseph Lehar; Martin Kang; Marcus A. Badgeley (CoRR, 2020)

Continuous action deep reinforcement learning for propofol dosing during general anesthesia. - Gabriel Schamberg; Marcus A. Badgeley; Benyamin Meschede-Krasa; Ohyoon Kwon; Emery N. Brown (Artif. Intell. Medicine, 2022)