Big omics data experience. - Patricia H. Kovatch; Anthony Costa; Zachary Giles; Eugene Fluder; Hyung Min Cho; Svetlana Mazurkova (SC, 2015)

Predictive Modeling of Hospital Readmission Rates Using Electronic Medical Record-Wide Machine Learning: A Case-Study Using Mount Sinai Heart Failure Cohort. - Khader Shameer; Kipp W. Johnson; Alexandre Yahi; Riccardo Miotto; Li Li 062; Doran Ricks; Jebakumar Jebakaran; Patricia H. Kovatch; Partho P. Sengupta; Annetine Gelijns; Alan Moskovitz; Bruce Darrow; et al. (PSB, 2017)

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)

Optimizing High-Performance Computing Systems for Biomedical Workloads. - Patricia H. Kovatch; Lili Gai; Hyung Min Cho; Eugene Fluder; Dansha Jiang (IPDPS Workshops, 2020)

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)

Computational Pathology at Health System Scale - Self-Supervised Foundation Models from Three Billion Images. - Gabriele Campanella; Ricky Kwan; Eugene Fluder; Jennifer Zeng; Aryeh Stock; Brandon Veremis; Alexandros D. Polydorides; Cyrus Hedvat; Adam Schoenfeld; Chad M. Vanderbilt; Patricia H. Kovatch; Carlos Cordon-Cardo; et al. (CoRR, 2023)

An AI-Guided Data Centric Strategy to Detect and Mitigate Biases in Healthcare Datasets. - Faris F. Gulamali; Ashwin S. Sawant; Lora Liharska; Carol R. Horowitz; Lili Chan; Patricia H. Kovatch; Ira S. Hofer; Karandeep Singh; Lynne D. Richardson; Emmanuel Mensah; Alexander W. Charney; David L. Reich; et al. (CoRR, 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)

Local large language models for privacy-preserving accelerated review of historic echocardiogram reports. - Akhil Vaid; Son Q. Duong; Joshua Lampert; Patricia H. Kovatch; Robert Freeman; Edgar Argulian; Lori Croft; Stamatios Lerakis; Martin Goldman; Rohan Khera; Girish N. Nadkarni (J. Am. Medical Informatics Assoc., 2024)

Derivation, external and clinical validation of a deep learning approach for detecting intracranial hypertension. - Faris F. Gulamali; Pushkala Jayaraman; Ashwin S. Sawant; Jacob Desman; Benjamin Fox; Annette Chang; Brian Y. Soong; Naveen Arivazagan; Alexandra S. Reynolds; Son Q. Duong; Akhil Vaid; Patricia H. Kovatch; et al. (npj Digit. Medicine, 2024)

Multimodal fusion learning for long QT syndrome pathogenic genotypes in a racially diverse population. - Joy Jiang; Ha My Thi Vy; Alexander Charney; Patricia H. Kovatch; Vivek Reddy; Pushkala Jayaraman; Ron Do; Rohan Khera; Sumeet Chugh; Deepak L. Bhatt; Akhil Vaid; Joshua Lampert; et al. (npj Digit. Medicine, 2024)

A distributional reinforcement learning model for optimal glucose control after cardiac surgery. - Jacob Desman; Zhang-Wei Hong; Moein Sabounchi; Ashwin S. Sawant; Jaskirat Gill; Ana C. Costa; Gagan Kumar; Rajeev Sharma; Arpeta Gupta; Paul McCarthy; Veena Nandwani; Doug Powell; et al. (npj Digit. Medicine, 2025)