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

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13719, 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

Journals

  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 2021;35(4):575 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 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
  16. Mann K, Good N, Fatehi F, Khanna S, Campbell V, Conway R, Sullivan C, Staib A, Joyce C, Cook D. Predicting Patient Deterioration: A Review of Tools in the Digital Hospital Setting. Journal of Medical Internet Research 2021;23(9):e28209 View
  17. Esmaeilzadeh S, Lane C, Gerberi D, Wakeam E, Pickering B, Herasevich V, Hyder J. Improving In-Hospital Patient Rescue: What Are Studies on Early Warning Scores Missing? A Scoping Review. Critical Care Explorations 2022;4(2):e0644 View
  18. Chen X, Chen H, Nan S, Kong X, Duan H, Zhu H. Dealing With Missing, Imbalanced, and Sparse Features During the Development of a Prediction Model for Sudden Death Using Emergency Medicine Data: Machine Learning Approach. JMIR Medical Informatics 2023;11:e38590 View
  19. Kern-Goldberger A, Ewing J, Polin M, D'Alton M, Friedman A, Goffman D. The Predictive Value of Vital Signs for Morbidity in Pregnancy: Evaluating and Optimizing Maternal Early Warning Systems. American Journal of Perinatology 2023;40(14):1590 View
  20. Lim H, Austin J, van der Vegt A, Rahimi A, Canfell O, Mifsud J, Pole J, Barras M, Hodgson T, Shrapnel S, Sullivan C. Toward a Learning Health Care System: A Systematic Review and Evidence-Based Conceptual Framework for Implementation of Clinical Analytics in a Digital Hospital. Applied Clinical Informatics 2022;13(02):339 View
  21. Fan X, Xue N, Han Z, Wang C, Ma H, Lu Y, Abdulhay E. Wavelet Transform Artificial Intelligence Algorithm-Based Data Mining Technology for Norovirus Monitoring and Early Warning. Journal of Healthcare Engineering 2021;2021:1 View
  22. Shen Z, Tang C, Hu Y, Cai Y, Chen H, Chen H, Liu Y, Xie N, Tan S. Survey of Nursing Staff’s Training on Early Warning Ability for Inpatients with “Three Infarcts and One Hemorrhage”. Evidence-Based Complementary and Alternative Medicine 2021;2021:1 View
  23. Naemi A, Schmidt T, Mansourvar M, Naghavi-Behzad M, Ebrahimi A, Wiil U. Machine learning techniques for mortality prediction in emergency departments: a systematic review. BMJ Open 2021;11(11):e052663 View
  24. Coombes C, Coombes K, Fareed N. Sequences of Events from the Electronic Medical Record and the Onset of Infection. Chemistry & Biodiversity 2022;19(11) View
  25. Nguyen M, Corbin C, Eulalio T, Ostberg N, Machiraju G, Marafino B, Baiocchi M, Rose C, Chen J. Developing machine learning models to personalize care levels among emergency room patients for hospital admission. Journal of the American Medical Informatics Association 2021;28(11):2423 View
  26. Aracena C, Villena F, Arias F, Dunstan J. Aplicaciones de aprendizaje automático en salud. Revista Médica Clínica Las Condes 2022;33(6):568 View
  27. Corbin C, Maclay R, Acharya A, Mony S, Punnathanam S, Thapa R, Kotecha N, Shah N, Chen J. DEPLOYR: a technical framework for deploying custom real-time machine learning models into the electronic medical record. Journal of the American Medical Informatics Association 2023;30(9):1532 View
  28. Yin Y. Prediction and analysis of time series data based on granular computing. Frontiers in Computational Neuroscience 2023;17 View
  29. Caratù M, Pigliautile I, Piselli C, Fabiani C. A perspective on managing cities and citizens' well-being through smart sensing data. Environmental Science & Policy 2023;147:169 View
  30. van der Vegt A, Campbell V, Mitchell I, Malycha J, Simpson J, Flenady T, Flabouris A, Lane P, Mehta N, Kalke V, Decoyna J, Es’haghi N, Liu C, Scott I. Systematic review and longitudinal analysis of implementing Artificial Intelligence to predict clinical deterioration in adult hospitals: what is known and what remains uncertain. Journal of the American Medical Informatics Association 2024;31(2):509 View
  31. Rajapaksha L, Vidanagamachchi S, Gunawardena S, Thambawita V. An Open-Access Dataset of Hospitalized Cardiac-Arrest Patients: Machine-Learning-Based Predictions Using Clinical Documentation. BioMedInformatics 2023;4(1):34 View
  32. Kononova Y, Abramyan L, Funkner A, Babenko A. Machine learning prediction of in-hospital recurrent infarction and cardiac death in patients with myocardial infarction. Informatics in Medicine Unlocked 2024;45:101443 View
  33. Dehua L. Design of a real-time health monitoring and prediction system for table tennis players based on optical detection sensor networks. Optical and Quantum Electronics 2024;56(4) View

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
  3. Aziz R, Jawed F, Khan S, Sundus H. Medical Robotics and AI-Assisted Diagnostics for a High-Tech Healthcare Industry. View