This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (1603) Machine Learning (1685) Electronic Health Records (1154) Decision Support for Health Professionals (1323) Artificial Intelligence (1665)

Published on in Vol 24, No 12 (2022): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42163, first published .
Interpretable Machine Learning Prediction of Drug-Induced QT Prolongation: Electronic Health Record Analysis

Interpretable Machine Learning Prediction of Drug-Induced QT Prolongation: Electronic Health Record Analysis

Interpretable Machine Learning Prediction of Drug-Induced QT Prolongation: Electronic Health Record Analysis

Authors of this article:

Steven T Simon1 Author Orcid Image ;   Katy E Trinkley2 Author Orcid Image ;   Daniel C Malone3 Author Orcid Image ;   Michael Aaron Rosenberg4 Author Orcid Image
Article Authors Cited by (8) Tweetations (6) Metrics

Journals

  1. Barrett C, Suzuki Y, Hussein S, Garg L, Tumolo A, Sandhu A, West J, Zipse M, Aleong R, Varosy P, Tzou W, Banaei‐Kashani F, Rosenberg M. Evaluation of Quantitative Decision‐Making for Rhythm Management of Atrial Fibrillation Using Tabular Q‐Learning. Journal of the American Heart Association 2023;12(9) View
  2. Raissi Dehkordi N, Raissi Dehkordi N, Karimi Toudeshki K, Farjoo M. Artificial Intelligence in Diagnosis of Long QT Syndrome: A Review of Current State, Challenges, and Future Perspectives. Mayo Clinic Proceedings: Digital Health 2024;2(1):21 View
  3. Seong D, Espinosa C, Aghaeepour N. Computational Approaches for Predicting Preterm Birth and Newborn Outcomes. Clinics in Perinatology 2024;51(2):461 View
  4. Zhang H, Tarabanis C, Jethani N, Goldstein M, Smith S, Chinitz L, Ranganath R, Aphinyanaphongs Y, Jankelson L. QTNet. JACC: Clinical Electrophysiology 2024;10(5):956 View
  5. Simon S, Lin M, Trinkley K, Aleong R, Rafaels N, Crooks K, Reiter M, Gignoux C, Rosenberg M, Aalto-Setala K. A polygenic risk score for the QT interval is an independent predictor of drug-induced QT prolongation. PLOS ONE 2024;19(6):e0303261 View
  6. Bos J, Liu K, Attia Z, Noseworthy P, Friedman P, Ackerman M. Deep Neural Network Analysis of the 12-Lead Electrocardiogram Distinguishes Patients With Congenital Long QT Syndrome From Patients With Acquired QT Prolongation. Mayo Clinic Proceedings 2025;100(2):276 View
  7. Guha A, Shah V, Nahle T, Singh S, Kunhiraman H, Shehnaz F, Nain P, Makram O, Mahmoudi M, Al-Kindi S, Madabhushi A, Shiradkar R, Daoud H. Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review. Current Cardiology Reports 2025;27(1) View
  8. Trinkley K, Simon S, Rosenberg M. Impact of an Alert-Based Inpatient Clinical Decision Support Tool to Prevent Drug-Induced Long QT Syndrome: Large-Scale, System-Wide Observational Study. Journal of Medical Internet Research 2025;27:e68256 View