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

Artificial Intelligence (2129) Decision Support for Health Professionals (1539) Machine Learning (2076) Clinical Information and Decision Making (1915) Clinical Informatics (1325)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43734, first published .
Explainable Machine Learning Techniques To Predict Amiodarone-Induced Thyroid Dysfunction Risk: Multicenter, Retrospective Study With External Validation

Explainable Machine Learning Techniques To Predict Amiodarone-Induced Thyroid Dysfunction Risk: Multicenter, Retrospective Study With External Validation

Explainable Machine Learning Techniques To Predict Amiodarone-Induced Thyroid Dysfunction Risk: Multicenter, Retrospective Study With External Validation

Authors of this article:

Ya-Ting Lu1 Author Orcid Image ;   Horng-Jiun Chao1 Author Orcid Image ;   Yi-Chun Chiang1, 2 Author Orcid Image ;   Hsiang-Yin Chen1, 2 Author Orcid Image
Article Authors Cited by (16) Tweetations (3) Metrics

Journals

  1. Yang X, Qiu H, Wang L, Wang X. Predicting Colorectal Cancer Survival Using Time-to-Event Machine Learning: Retrospective Cohort Study. Journal of Medical Internet Research 2023;25:e44417 View
  2. Nasarian E, Alizadehsani R, Acharya U, Tsui K. Designing interpretable ML system to enhance trust in healthcare: A systematic review to proposed responsible clinician-AI-collaboration framework. Information Fusion 2024;108:102412 View
  3. Olejarz M, Szczepanek-Parulska E, Ruchala M. Lipoprotein alterations in endocrine disorders - a review of the recent developments in the field. Frontiers in Endocrinology 2024;15 View
  4. Qu Z, Wang Y, Guo D, He G, Sui C, Duan Y, Zhang X, Meng H, Lan L, Liu X. Comparison of deep learning models to traditional Cox regression in predicting survival of colon cancer: Based on the SEER database. Journal of Gastroenterology and Hepatology 2024;39(9):1816 View
  5. Sutradhar A, Al Rafi M, Ghosh P, Shamrat F, Moniruzzaman M, Ahmed K, Azad A, Bui F, Chen L, Moni M. An Intelligent Thyroid Diagnosis System Utilizing Multiple Ensemble and Explainable Algorithms With Medical Supported Attributes. IEEE Transactions on Artificial Intelligence 2024;5(6):2840 View
  6. Ermolaeva A, Fadeev V. Type 2 amiodarone-induced thyrotoxicosis: prevalence, time and predictors of development. Problems of Endocrinology 2023;70(3):9 View
  7. Zhou H, Fang C, Pan Y. Development of a System for Predicting Hospitalization Time for Patients With Traumatic Brain Injury Based on Machine Learning Algorithms: User-Centered Design Case Study. JMIR Human Factors 2024;11:e62866 View
  8. Almomani A, Obeidat M, Khassawneh M, Maayeh S, Al-Malouf K. Efficacy and Safety of Amiodarone and Propranolol in Pediatric Cardiology for Arrhythmia After Cardiac Surgery: A Retrospective Design Study. Cureus 2024 View
  9. Hu Q, Chen Y, Zou D, He Z, Xu T. Predicting adverse drug event using machine learning based on electronic health records: a systematic review and meta-analysis. Frontiers in Pharmacology 2024;15 View
  10. Wong A, Flanagan T, Covington E, Nguyen E, Linn D, Brummel G, Hoffmaster B, Isaacs D, Kane‐Gill S. Forecasting the impact of artificial intelligence on clinical pharmacy practice. JACCP: JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY 2025;8(4):302 View
  11. Alahmadi M. A Risk-Optimized Framework for Data-Driven IPO Underperformance Prediction in Complex Financial Systems. Systems 2025;13(3):179 View
  12. Chan F, Ku Y, Lie W, Chen H. Web-Based Explainable Machine Learning-Based Drug Surveillance for Predicting Sunitinib- and Sorafenib-Associated Thyroid Dysfunction: Model Development and Validation Study. JMIR Formative Research 2025;9:e67767 View
  13. Costa F, Gomez Doblas J, Díaz Expósito A, Adamo M, D’Ascenzo F, Kołtowski L, Saba L, Mendieta G, Gragnano F, Calabrò P, Badimon L, Ibañez B, Mehran R, Angiolillo D, Lüscher T, Capodanno D. Artificial intelligence in cardiovascular pharmacotherapy: applications and perspectives. European Heart Journal 2025;46(37):3616 View
  14. Kamińska M, Trofimiuk-Müldner M, Sokołowski G, Hubalewska-Dydejczyk A. Machine learning in endocrinology: current applications and future perspectives. Endocrine 2025;90(2):357 View

Books/Policy Documents

  1. Gao M, Oliva A, Merhan R. The First Steps of Artificial Intelligence in Cardiology. View

Conference Proceedings

  1. Singh G, Kumar N, Kumar S. 2024 Eighth International Conference on Parallel, Distributed and Grid Computing (PDGC). Sick Euthyroid Detection Using Machine Learning Techniques View