Published on in Vol 8, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19489, first published .
Machine Learning Approach to Reduce Alert Fatigue Using a Disease Medication–Related Clinical Decision Support System: Model Development and Validation

Machine Learning Approach to Reduce Alert Fatigue Using a Disease Medication–Related Clinical Decision Support System: Model Development and Validation

Machine Learning Approach to Reduce Alert Fatigue Using a Disease Medication–Related Clinical Decision Support System: Model Development and Validation

Journals

  1. Islam M, Poly T, Alsinglawi B, Lin L, Chien S, Liu J, Jian W. Application of Artificial Intelligence in COVID-19 Pandemic: Bibliometric Analysis. Healthcare 2021;9(4):441 View
  2. Liu S, Kawamoto K, Del Fiol G, Weir C, Malone D, Reese T, Morgan K, ElHalta D, Abdelrahman S. The potential for leveraging machine learning to filter medication alerts. Journal of the American Medical Informatics Association 2022;29(5):891 View
  3. Park H, Chae M, Jeong W, Yu J, Jung W, Chang H, Cha W. Appropriateness of Alerts and Physicians’ Responses With a Medication-Related Clinical Decision Support System: Retrospective Observational Study. JMIR Medical Informatics 2022;10(10):e40511 View
  4. Ali S, Jung S, Bilal H, Lee S, Hussain J, Afzal M, Hussain M, Ali T, Chung T, Lee S. Clinical Decision Support System Based on Hybrid Knowledge Modeling: A Case Study of Chronic Kidney Disease-Mineral and Bone Disorder Treatment. International Journal of Environmental Research and Public Health 2021;19(1):226 View
  5. Lee N, Anastos-Wallen R, Chaiyachati K, Reitz C, Asch D, Mehta S. Clinician Decisions After Notification of Elevated Blood Pressure Measurements From Patients in a Remote Monitoring Program. JAMA Network Open 2022;5(1):e2143590 View
  6. Kim S, Kim E, Kim H. Physician Knowledge Base: Clinical Decision Support Systems. Yonsei Medical Journal 2022;63(1):8 View
  7. Hsu J, Nguyen P, Phuc P, Lo T, Hsu M, Hsieh M, Le N, Cheng C, Chang T, Chen C. Development and Validation of Novel Deep-Learning Models Using Multiple Data Types for Lung Cancer Survival. Cancers 2022;14(22):5562 View
  8. Yalçın N, Kaşıkcı M, Çelik H, Allegaert K, Demirkan K, Yiğit Ş, Yurdakök M. Development and validation of a machine learning-based detection system to improve precision screening for medication errors in the neonatal intensive care unit. Frontiers in Pharmacology 2023;14 View
  9. Vijayakumar S, Lee V, Leong Q, Hong S, Blasiak A, Ho D. Physicians’ Perspectives on AI in Clinical Decision Support Systems: Interview Study of the CURATE.AI Personalized Dose Optimization Platform. JMIR Human Factors 2023;10:e48476 View
  10. Chen C, Chen Y, Scholl J, Yang H, Li Y. Ability of machine-learning based clinical decision support system to reduce alert fatigue, wrong-drug errors, and alert users about look alike, sound alike medication. Computer Methods and Programs in Biomedicine 2024;243:107869 View
  11. Quan P, Sánchez-Fernández S, Parrado Gil L, Calvo Alonso A, Bodero Sánchez J, Ortega Eslava A, Luri M, Gastaminza Lasarte G. Usefulness of Drug Allergy Alert Systems: Present and Future. Current Treatment Options in Allergy 2023;10(4):413 View

Books/Policy Documents

  1. Kalina J. Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems. View
  2. Hsu J, Lu C. Encyclopedia of Evidence in Pharmaceutical Public Health and Health Services Research in Pharmacy. View
  3. Rocha R, Maviglia S, Rocha B. Clinical Decision Support and Beyond. View
  4. Hsu J, Lu C. Encyclopedia of Evidence in Pharmaceutical Public Health and Health Services Research in Pharmacy. View