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JMIR Medical Informatics|August 29, 2025
A Machine Learning Approach for Identifying People With Neuroinfectious Diseases in Electronic Health Records: Algorithm Development and ValidationArjun Singh, Shadi Sartipi, Haoqi Sun, et al.
JMIR Medical Informatics|September 2, 2025
Resilience in the Face of Disruption: Viewpoint on the CrowdStrike Incident in July 2024Christopher R Dennis, Christopher S Evans, Kathleen Duckworth, et al.
JMIR Medical Informatics|April 30, 2020
Modified Bidirectional Encoder Representations From Transformers Extractive Summarization Model for Hospital Information Systems Based on Character-Level Tokens (AlphaBERT): Development and Performance EvaluationYen-Pin Chen, Yi-Ying Chen, Jr-Jiun Lin, et al.
JMIR Medical Informatics|April 30, 2020
Prediction of Preeclampsia and Intrauterine Growth Restriction: Development of Machine Learning Models on a Prospective CohortHerdiantri Sufriyana, Yu-Wei Wu, Emily Chia-Yu Su
JMIR Medical Informatics|May 16, 2020
Clinical Desire for an Artificial Intelligence-Based Surgical Assistant System: Electronic Survey-Based StudySoo Jin Park, Eun Ji Lee, Se Ik Kim, et al.
JMIR Medical Informatics|May 27, 2015
Comprehensive evaluation of electronic medical record system use and user satisfaction at five low-resource setting hospitals in ethiopiaBinyam Tilahun, Fleur Fritz
JMIR Medical Informatics|June 12, 2015
A Web-Based Tool for Patient Triage in Emergency Department Settings: Validation Using the Emergency Severity IndexPierre Elias, Ash Damle, Michael Casale, et al.
JMIR Medical Informatics|July 23, 2016
Forecasting Daily Patient Outflow From a Ward Having No Real-Time Clinical DataShivapratap Gopakumar, Truyen Tran, Wei Luo, et al.
JMIR Medical Informatics|April 15, 2020
A Novel, Integrative Approach for Evaluating Progression in Multiple Sclerosis: Development of a Scoring AlgorithmChloe Tolley, Daniela Piani-Meier, Sarah Bentley, et al.
JMIR Medical Informatics|May 6, 2020
Determining Factors Affecting Nurses' Acceptance of a Care Plan System Using a Modified Technology Acceptance Model 3: Structural Equation Model With Cross-Sectional DataKuei-Fang Ho, Pi-Chen Chang, Maria Dyah Kurniasari, et al.
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