An Integrative Pipeline for Multi-Modal Discovery of Disease Relationships. - Benjamin S. Glicksberg; Li Li 062; Wei-Yi Cheng; Khader Shameer; Jörg Hakenberg; Rafael Castellanos; Meng Ma; Lisong Shi; Hardik Shah; Joel T. Dudley; Rong Chen 006 (Pacific Symposium on Biocomputing, 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)

Identify Cancer Driver Genes Through Shared Mendelian Disease Pathogenic Variants and Cancer Somatic Mutations. - Meng Ma; Changchang Wang; Benjamin S. Glicksberg; Eric E. Schadt; Shuyu Dan Li; Rong Chen 006 (PSB, 2017)

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)

Predicting age by mining electronic medical records with deep learning characterizes differences between chronological and physiological age. - Zichen Wang 002; Li Li 062; Benjamin S. Glicksberg; Ariel Israel; Joel T. Dudley; Avi Ma'ayan (J. Biomed. Informatics, 2017)

Automatic processing of Electronic Medical Records using Deep Learning. - Venet Osmani; Li Li 062; Matteo Danieletto; Benjamin S. Glicksberg; Joel Dudley; Oscar Mayora (PervasiveHealth, 2018)

Automated disease cohort selection using word embeddings from Electronic Health Records. - Benjamin S. Glicksberg; Riccardo Miotto; Kipp W. Johnson; Khader Shameer; Li Li 062; Rong Chen 006; Joel T. Dudley (PSB, 2018)

Causal inference on electronic health records to assess blood pressure treatment targets: An application of the parametric g formula. - Kipp W. Johnson; Benjamin S. Glicksberg; Rachel Hodos; Khader Shameer; Joel T. Dudley (PSB, 2018)

Loss-of-function of neuroplasticity-related genes confers risk for human neurodevelopmental disorders. - Milo R. Smith; Benjamin S. Glicksberg; Li Li 062; Rong Chen 006; Hirofumi Morishita; Joel T. Dudley (PSB, 2018)

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)

Processing of Electronic Health Records using Deep Learning: A review. - Venet Osmani; Li Li 062; Matteo Danieletto; Benjamin S. Glicksberg; Joel Dudley; Oscar Mayora (CoRR, 2018)

Time Aggregation and Model Interpretation for Deep Multivariate Longitudinal Patient Outcome Forecasting Systems in Chronic Ambulatory Care. - Beau Norgeot; Dmytro Lituiev; Benjamin S. Glicksberg; Atul J. Butte (CoRR, 2018)

Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining. - Khader Shameer; M. Mercedes Perez-Rodriguez; Roy Bachar; Li Li 062; Amy Johnson; Kipp W. Johnson; Benjamin S. Glicksberg; Milo R. Smith; Ben Readhead; Joseph Scarpa; Jebakumar Jebakaran; Patricia H. Kovatch; et al. (BMC Medical Informatics Decis. Mak., 2018)

Evaluation of patient re-identification using laboratory test orders and mitigation via latent space variables. - Kipp W. Johnson; Jessica K. De Freitas; Benjamin S. Glicksberg; Jason R. Bobe; Joel T. Dudley (PSB, 2019)

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)

Robust prediction of clinical outcomes using cytometry data. - Zicheng Hu; Benjamin S. Glicksberg; Atul J. Butte (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)

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)

Sepsis in the era of data-driven medicine: personalizing risks, diagnoses, treatments and prognoses. - Andrew C. Liu; Krishna Patel; Ramya Dhatri Vunikili; Kipp W. Johnson; Fahad Abdu; Shivani Kamath Belman; Benjamin S. Glicksberg; Pratyush Tandale; Roberto Fontanez; Oommen K. Mathew; Andrew Kasarskis; Priyabrata Mukherjee; et al. (Briefings Bioinform., 2020)

Identification of therapeutic targets from genetic association studies using hierarchical component analysis. - Hao-Chih Lee; Osamu Ichikawa; Benjamin S. Glicksberg; Aparna A. Divaraniya; Christine E. Becker; Pankaj Agarwal; Joel T. Dudley (BioData Min., 2020)

Deep representation learning of electronic health records to unlock patient stratification at scale. - Isotta Landi; Benjamin S. Glicksberg; Hao-Chih Lee; Sarah T. Cherng; Giulia Landi; Matteo Danieletto; Joel T. Dudley; Cesare Furlanello; Riccardo Miotto (npj Digit. Medicine, 2020)

Protected Health Information filter (Philter): accurately and securely de-identifying free-text clinical notes. - Beau Norgeot; Kathleen Muenzen; Thomas A. Peterson; Xuancheng Fan; Benjamin S. Glicksberg; Gundolf Schenk; Eugenia Rutenberg; Boris Oskotsky; Marina Sirota; Jinoos Yazdany; Gabriela Schmajuk; Dana Ludwig; et al. (npj Digit. Medicine, 2020)

Sleep in the Natural Environment: A Pilot Study. - Fayzan F. Chaudhry; Matteo Danieletto; Eddye Golden; Jerome R. Scelza; Greg Botwin; Mark M. Shervey; Jessica K. De Freitas; Ishan Paranjpe; Girish N. Nadkarni; Riccardo Miotto; Patricia Glowe; Greg Stock; et al. (Sensors, 2020)

Extracting Social Isolation Information From Psychiatric Notes in the Electronic Health Records. - Lauren A. Lepow; Braja Gopal Patra; Isotta Landi; Prakash Adekkanattu; Jyotishman Pathak; Mark Olfson; J. John Mann; Euijung Ryu; Joanna M. Biernacka; Girish N. Nadkarni; Priya Wickramaratne; Myrna Weissman; et al. (AMIA, 2021)

Multi-Modal Data Science for Healthcare: State of the Art, Challenges, and Opportunities. - Yuan Luo 001; Fei Wang 001; Benjamin S. Glicksberg; Jessilyn Dunn; Nigam Shah (AMIA, 2021)

A Simple Free-Text-like Method for Extracting Semi-Structured Data from Electronic Health Records: Exemplified in Prediction of In-Hospital Mortality. - Eyal Klang; Matthew A. Levin; Shelly Soffer; Alexis Zebrowski; Benjamin S. Glicksberg; Brendan G. Carr; Jolion Mcgreevy; David L. Reich; Robert Freeman (Big Data Cogn. Comput., 2021)

Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients. - Tingyi Wanyan; Hossein Honarvar; Suraj K. Jaladanki; Chengxi Zang; Nidhi Naik; Sulaiman Somani; Jessica K. De Freitas; Ishan Paranjpe; Akhil Vaid; Riccardo Miotto; Girish N. Nadkarni; Marinka Zitnik; et al. (CoRR, 2021)

Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data. - Tingyi Wanyan; Jing Zhang; Ying Ding 001; Ariful Azad; Zhangyang Wang; Benjamin S. Glicksberg (CoRR, 2021)

Cross-Modal Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop. - Yan Han 001; Chongyan Chen; Ahmed H. Tewfik; Benjamin S. Glicksberg; Ying Ding 001; Yifan Peng 002; Zhangyang Wang (CoRR, 2021)

Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health. - Ryan T. Scott; Erik L. Antonsen; Lauren M. Sanders; Jaden J. A. Hastings; Seung-Min Park; Graham Mackintosh; Robert J. Reynolds; Adrienne L. Hoarfrost; Aenor Sawyer; Casey S. Greene; Benjamin S. Glicksberg; Corey A. Theriot; et al. (CoRR, 2021)

Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs. - Lauren M. Sanders; Jason H. Yang; Ryan T. Scott; Amina Ann Qutub; Héctor García Martín; Daniel C. Berrios; Jaden J. A. Hastings; Jon Rask; Graham Mackintosh; Adrienne L. Hoarfrost; Stuart J. Chalk; John Kalantari; et al. (CoRR, 2021)

Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records. - Tingyi Wanyan; Hossein Honarvar; Ariful Azad; Ying Ding 001; Benjamin S. Glicksberg (Data Intell., 2021)

Predictive Modelling of Susceptibility to Substance Abuse, Mortality and Drug-Drug Interactions in Opioid Patients. - Ramya Dhatri Vunikili; Benjamin S. Glicksberg; Kipp W. Johnson; Joel T. Dudley; Lakshminarayanan Subramanian; Khader Shameer (Frontiers Artif. Intell., 2021)

Extracting social determinants of health from electronic health records using natural language processing: a systematic review. - Braja Gopal Patra; Mohit Manoj Sharma; Veer Vekaria; Prakash Adekkanattu; Olga V. Patterson; Benjamin S. Glicksberg; Lauren A. Lepow; Euijung Ryu; Joanna M. Biernacka; Al'ona Furmanchuk; Thomas J. George; William R. Hogan; et al. (J. Am. Medical Informatics Assoc., 2021)

Federated Learning for Healthcare Informatics. - Jie Xu 012; Benjamin S. Glicksberg; Chang Su 002; Peter B. Walker; Jiang Bian 001; Fei Wang 001 (J. Heal. Informatics Res., 2021)

Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records. - Jessica K. De Freitas; Kipp W. Johnson; Eddye Golden; Girish N. Nadkarni; Joel T. Dudley; Erwin P. Bottinger; Benjamin S. Glicksberg; Riccardo Miotto (Patterns, 2021)

Contrastive learning improves critical event prediction in COVID-19 patients. - Tingyi Wanyan; Hossein Honarvar; Suraj K. Jaladanki; Chengxi Zang; Nidhi Naik; Sulaiman Somani; Jessica K. De Freitas; Ishan Paranjpe; Akhil Vaid; Jing Zhang; Riccardo Miotto; Zhangyang Wang; et al. (Patterns, 2021)

Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit. - Tingyi Wanyan; Akhil Vaid; Jessica K. De Freitas; Sulaiman Somani; Riccardo Miotto; Girish N. Nadkarni; Ariful Azad; Ying Ding 001; Benjamin S. Glicksberg (IEEE Trans. Big Data, 2021)

Defining an Ethical Relationship with Artificial Intelligence at a Large Academic Health System. - Teja Ganta; Nicholas Genes; Benjamin S. Glicksberg; Rosamond Rhodes; Robbie Freeman; Bruce Darrow (AMIA, 2022)

Supervised pretraining through contrastive categorical positive samplings to improve COVID-19 mortality prediction. - Tingyi Wanyan; Mingquan Lin; Eyal Klang; Kartikeya M. Menon; Faris F. Gulamali; Ariful Azad; Yiye Zhang; Ying Ding 001; Zhangyang Wang; Fei Wang 001; Benjamin S. Glicksberg; Yifan Peng 002 (BCB, 2022)

Predicting Age-related Macular Degeneration Progression with Longitudinal Fundus Images Using Deep Learning. - Junghwan Lee; Tingyi Wanyan; Qingyu Chen 001; Tiarnan D. L. Keenan; Benjamin S. Glicksberg; Emily Y. Chew; Zhiyong Lu; Fei Wang 001; Yifan Peng 002 (MLMI@MICCAI, 2022)

Multi-Dimensional Laboratory Test Score as a Proxy for Health. - Bar H. Ezra; Shreyas Havaldar; Benjamin S. Glicksberg; Nadav Rappoport (MIE, 2022)

Knowledge-Augmented Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop. - Yan Han 001; Chongyan Chen; Ahmed H. Tewfik; Benjamin S. Glicksberg; Ying Ding 001; Yifan Peng 002; Zhangyang Wang (WACV, 2022)

HeartBEiT: Vision Transformer for Electrocardiogram Data Improves Diagnostic Performance at Low Sample Sizes. - Akhil Vaid; Joy Jiang; Ashwin Sawant; Stamatios Lerakis; Edgar Argulian; Yuri Ahuja; Joshua Lampert; Alexander Charney; Hayit Greenspan; Benjamin S. Glicksberg; Jagat Narula; Girish N. Nadkarni (CoRR, 2022)

Autoencoders for sample size estimation for fully connected neural network classifiers. - Faris F. Gulamali; Ashwin Sawant; Patricia H. Kovatch; Benjamin S. Glicksberg; Alexander Charney; Girish N. Nadkarni; Eric K. Oermann (npj Digit. Medicine, 2022)

Optimizing Embedding Space with Sub-categorical Supervised Pre-training: A Theoretical Approach Towards Improving Sepsis Prediction. - Tingyi Wanyan; Mingquan Lin; Ying Ding 001; Benjamin S. Glicksberg; Fei Wang 001; Yifan Peng 002 (ICHI, 2023)

Online Unsupervised Representation Learning of Waveforms in the Intensive Care Unit via a novel cooperative framework: Spatially Resolved Temporal Networks (SpaRTEn). - Faris F. Gulamali; Ashwin Sawant; Ira S. Hofer; Matthew A. Levin; Alexander Charney; Karandeep Singh; Benjamin S. Glicksberg; Girish N. Nadkarni (MLHC, 2023)

Generation of a Compendium of Transcription Factor Cascades and Identification of Potential Therapeutic Targets using Graph Machine Learning. - Sonish Sivarajkumar; Pratyush Tandale; Ankit Bhardwaj; Kipp W. Johnson; Anoop Titus; Benjamin S. Glicksberg; Shameer Khader; Kamlesh K. Yadav; Lakshminarayanan Subramanian (CoRR, 2023)

A foundational vision transformer improves diagnostic performance for electrocardiograms. - Akhil Vaid; Joy Jiang; Ashwin Sawant; Stamatios Lerakis; Edgar Argulian; Yuri Ahuja; Joshua Lampert; Alexander Charney; Hayit Greenspan; Jagat Narula; Benjamin S. Glicksberg; Girish N. Nadkarni (npj Digit. Medicine, 2023)

Editorial: Explainable artificial intelligence for critical healthcare applications. - Zhe He 001; Rui Zhang 028; Gayo Diallo; Zhengxing Huang; Benjamin S. Glicksberg (Frontiers Artif. Intell., 2023)

Biological research and self-driving labs in deep space supported by artificial intelligence. - Lauren M. Sanders; Ryan T. Scott; Jason H. Yang; Amina Ann Qutub; Héctor García Martín; Daniel C. Berrios; Jaden J. A. Hastings; Jon Rask; Graham Mackintosh; Adrienne L. Hoarfrost; Stuart J. Chalk; John Kalantari; et al. (Nat. Mac. Intell., 2023)

Biomonitoring and precision health in deep space supported by artificial intelligence. - Ryan T. Scott; Lauren M. Sanders; Erik L. Antonsen; Jaden J. A. Hastings; Seung-Min Park; Graham Mackintosh; Robert J. Reynolds; Adrienne L. Hoarfrost; Aenor Sawyer; Casey S. Greene; Benjamin S. Glicksberg; Corey A. Theriot; et al. (Nat. Mac. Intell., 2023)

A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19. - Wonsuk Oh; Pushkala Jayaraman; Pranai Tandon; Udit S. Chaddha; Patricia H. Kovatch; Alexander W. Charney; Benjamin S. Glicksberg; Girish N. Nadkarni (Artif. Intell. Medicine, 2024)

Generative Large Language Models are autonomous practitioners of evidence-based medicine. - Akhil Vaid; Joshua Lampert; Juhee Lee; Ashwin Sawant; Donald Apakama; Ankit Sakhuja; Ali Soroush; Denise Lee; Isotta Landi; Nicole Bussola; Ismail Nabeel; Robbie Freeman; et al. (CoRR, 2024)

Cloud Platforms for Developing Generative AI Solutions: A Scoping Review of Tools and Services. - Dhavalkumar Patel; Ganesh Raut; Satya Narayan Cheetirala; Girish N. Nadkarni; Robert Freeman; Benjamin S. Glicksberg; Eyal Klang; Prem Timsina (CoRR, 2024)

Evaluating the accuracy of a state-of-the-art large language model for prediction of admissions from the emergency room. - Benjamin S. Glicksberg; Prem Timsina; Dhaval Patel; Ashwin Sawant; Akhil Vaid; Ganesh Raut; Alexander W. Charney; Donald Apakama; Brendan G. Carr; Robert Freeman; Girish N. Nadkarni; Eyal Klang (J. Am. Medical Informatics Assoc., 2024)

A strategy for cost-effective large language model use at health system-scale. - Eyal Klang; Donald Apakama; Ethan E. Abbott; Akhil Vaid; Joshua Lampert; Ankit Sakhuja; Robert Freeman; Alexander W. Charney; David Reich; Monica Kraft; Girish N. Nadkarni; Benjamin S. Glicksberg (npj Digit. Medicine, 2024)

ChatGPT: A Conceptual Review of Applications and Utility in the Field of Medicine. - Shiavax J. Rao; Ameesh Isath; Parvathy Krishnan; Jonathan A. Tangsrivimol; Hafeez Ul Hassan Virk; Zhen Wang; Benjamin S. Glicksberg; Chayakrit Krittanawong (J. Medical Syst., 2024)

Refining LLMs outputs with iterative consensus ensemble (ICE). - Mahmud Omar; Benjamin S. Glicksberg; Girish N. Nadkarni; Eyal Klang (Comput. Biol. Medicine, 2025)

Generating credible referenced medical research: A comparative study of openAI's GPT-4 and Google's gemini. - Mahmud Omar; Saleh Nassar; Kareem Hijazi; Benjamin S. Glicksberg; Girish N. Nadkarni; Eyal Klang (Comput. Biol. Medicine, 2025)

DeepSeek vs. ChatGPT: prospects and challenges. - Inhye Jin; Jonathan A. Tangsrivimol; Erfan Darzi; Hafeez Ul Hassan Virk; Zhen Wang; Jan Egger; Sean Hacking; Benjamin S. Glicksberg; Markus Strauss; Chayakrit Krittanawong (Frontiers Artif. Intell., 2025)

Benefits, limits, and risks of ChatGPT in medicine. - Jonathan A. Tangsrivimol; Erfan Darzidehkalani; Hafeez Ul Hassan Virk; Zhen Wang; Jan Egger; Michelle Wang; Sean Hacking; Benjamin S. Glicksberg; Markus Strauss; Chayakrit Krittanawong (Frontiers Artif. Intell., 2025)