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Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/79307, first published .
Doctor's desk with computer showing patient appointments, thermometer, and stethoscope

Methods for Addressing Missingness in Electronic Health Record Data for Clinical Prediction Models: Comparative Evaluation

Methods for Addressing Missingness in Electronic Health Record Data for Clinical Prediction Models: Comparative Evaluation

Journals

  1. Arikhad M, Tariq A, Rasool S. Risk Analysis in Cardiovascular Healthcare Systems Using Machine Learning for Better Clinical Decision Support. International Journal of Innovative Research in Computer Science and Technology 2026;14(2):34 View
  2. Salim S, Ibrahim A. A Machine Learning Approach for Predicting 30-Day Hospital Readmission in Patients with Diabetes. Healthcare 2026;14(9):1185 View
  3. Mehal K, Pielich W, Siusta N, Gałan K, Kuczyńska S, Balicka-Dworczak N. ARTIFICIAL INTELLIGENCE AND THE DIAGNOSTIC INVISIBILITY OF WOMEN’S METABOLIC DISORDERS: A NARRATIVE REVIEW OF SOCIO-TECHNICAL CHALLENGES IN PCOS/PMOS RECOGNITION. International Journal of Innovative Technologies in Social Science 2026;3(2(50)) View
  4. Ou Y, Chen C, Wang J. MedFusion-Agent: A Multi-Agent Framework for Conflict-Aware Clinical Pharmacotherapy Planning in Multimorbidity. IEEE Access 2026;14:93913 View
  5. Chen F, Fu Q, Li L, Zhang X, Ge Y, Chen J. Development and validation of an interpretable machine learning model for predicting atrial fibrillation risk in middle-aged and older patients with coronary heart disease. Frontiers in Cardiovascular Medicine 2026;13 View
  6. Willis C, Goodin D, Miller H, Suliman S, Frieboes H. Evaluation of accuracy and potential biases of imputation methods on biomedical datasets subject to different mechanisms and patterns of missingness. Computer Methods and Programs in Biomedicine Update 2026;10:100263 View