Published on in Vol 21 , No 7 (2019) :July

Preprints (earlier versions) of this paper are available at, first published .
A Real-Time Early Warning System for Monitoring Inpatient Mortality Risk: Prospective Study Using Electronic Medical Record Data

A Real-Time Early Warning System for Monitoring Inpatient Mortality Risk: Prospective Study Using Electronic Medical Record Data

A Real-Time Early Warning System for Monitoring Inpatient Mortality Risk: Prospective Study Using Electronic Medical Record Data


  1. Ye C, Li J, Hao S, Liu M, Jin H, Zheng L, Xia M, Jin B, Zhu C, Alfreds S, Stearns F, Kanov L, Sylvester K, Widen E, McElhinney D, Ling X. Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm. International Journal of Medical Informatics 2020;137:104105 View
  2. . A Path for Translation of Machine Learning Products into Healthcare Delivery. EMJ Innovations 2020 View
  3. Kwan J, Lo L, Ferguson J, Goldberg H, Diaz-Martinez J, Tomlinson G, Grimshaw J, Shojania K. Computerised clinical decision support systems and absolute improvements in care: meta-analysis of controlled clinical trials. BMJ 2020:m3216 View
  4. Mcneill H, Khairat S. Impact of Intensive Care Unit Readmissions on Patient Outcomes and the Evaluation of the National Early Warning Score to Prevent Readmissions: Literature Review. JMIR Perioperative Medicine 2020;3(1):e13782 View
  5. Choudhury A, Asan O. Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review. JMIR Medical Informatics 2020;8(7):e18599 View
  6. Rosero E, Romito B, Joshi G. Failure to rescue: A quality indicator for postoperative care. Best Practice & Research Clinical Anaesthesiology 2020 View
  7. Diao X, Huo Y, Yan Z, Wang H, Yuan J, Wang Y, Cai J, Zhao W. An Application of Machine Learning to Etiological Diagnosis of Secondary Hypertension: Retrospective Study Using Electronic Medical Records. JMIR Medical Informatics 2021;9(1):e19739 View
  8. Klumpner T, Massarweh N, Kheterpal S. Opportunities to Improve the Capacity to Rescue. Anesthesiology Clinics 2020;38(4):775 View
  9. Schwartz J, Moy A, Rossetti S, Elhadad N, Cato K. Clinician involvement in research on machine learning–based predictive clinical decision support for the hospital setting: A scoping review. Journal of the American Medical Informatics Association 2021;28(3):653 View
  10. Mahendraker N, Flanagan M, Azar J, Williams L. Development and Validation of a 30-Day In-hospital Mortality Model Among Seriously Ill Transferred Patients: a Retrospective Cohort Study. Journal of General Internal Medicine 2021;36(8):2244 View
  11. Watari T. 2. Assessment of Abnormal Vital Signs-as a Strong Predictor of Emergency Care-. Nihon Naika Gakkai Zasshi 2019;108(12):2460 View
  12. Zhang Y, Han Y, Gao P, Mo Y, Hao S, Huang J, Ye F, Li Z, Zheng L, Yao X, Li Z, Li X, Wang X, Huang C, Jin B, Zhang Y, Yang G, Alfreds S, Kanov L, Sylvester K, Widen E, Li L, Ling X. Electronic Health Record–Based Prediction of 1-Year Risk of Incident Cardiac Dysrhythmia: Prospective Case-Finding Algorithm Development and Validation Study. JMIR Medical Informatics 2021;9(2):e23606 View
  13. Møller J, Sørensen M, Hardahl C, Pappalardo F. Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: A retrospective cohort study. PLOS ONE 2021;16(3):e0248636 View
  14. Romero-Brufau S, Whitford D, Johnson M, Hickman J, Morlan B, Therneau T, Naessens J, Huddleston J. Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS). Journal of the American Medical Informatics Association 2021;28(6):1207 View
  15. Ronzio L, Cabitza F, Barbaro A, Banfi G. Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine. Diagnostics 2021;11(2):372 View
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Books/Policy Documents

  1. Garcia Henao J, Precioso F, Staccini P, Riveill M. IT Convergence and Security. View
  2. Ulapane N, Wickramasinghe N. Optimizing Health Monitoring Systems With Wireless Technology. View