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

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