Published on in Vol 23, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25187, first published .
Machine Learning–Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review

Machine Learning–Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review

Machine Learning–Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review

Journals

  1. Martinez-Millana A, Saez-Saez A, Tornero-Costa R, Azzopardi-Muscat N, Traver V, Novillo-Ortiz D. Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. International Journal of Medical Informatics 2022;166:104855 View
  2. Mendo I, Marques G, de la Torre Díez I, López-Coronado M, Martín-Rodríguez F. Machine Learning in Medical Emergencies: a Systematic Review and Analysis. Journal of Medical Systems 2021;45(10) View
  3. Liu C, Hung C, Ko S, Cheng K, Chao C, Sung M, Hsing S, Wang J, Chen C, Lai C, Chen C, Chiu C. An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approach. Frontiers in Medicine 2022;9 View
  4. Luo J, Lan L, Huang S, Zeng X, Xiang Q, Li M, Yang S, Zhao W, Zhou X. Real-time prediction of organ failures in patients with acute pancreatitis using longitudinal irregular data. Journal of Biomedical Informatics 2023;139:104310 View
  5. Park J, Hsu T, Hu J, Chen C, Hsu W, Lee M, Ho J, Lee C. Predicting Sepsis Mortality in a Population-Based National Database: Machine Learning Approach. Journal of Medical Internet Research 2022;24(4):e29982 View
  6. Hobensack M, Song J, Scharp D, Bowles K, Topaz M. Machine learning applied to electronic health record data in home healthcare: A scoping review. International Journal of Medical Informatics 2023;170:104978 View
  7. Riessen R, Haap M, Hellwege R. Intensivmedizinisches Monitoring. DMW - Deutsche Medizinische Wochenschrift 2022;147(01/02):34 View
  8. Maviglia R, Michi T, Passaro D, Raggi V, Bocci M, Piervincenzi E, Mercurio G, Lucente M, Murri R. Machine Learning and Antibiotic Management. Antibiotics 2022;11(3):304 View
  9. Choi A, Chung K, Chung S, Lee K, Hyun H, Kim J. Advantage of Vital Sign Monitoring Using a Wireless Wearable Device for Predicting Septic Shock in Febrile Patients in the Emergency Department: A Machine Learning-Based Analysis. Sensors 2022;22(18):7054 View
  10. Saab A, Abi Khalil C, Jammal M, Saikali M, Lamy J. Early Prediction of All-Cause Clinical Deterioration in General Wards Patients: Development and Validation of a Biomarker-Based Machine Learning Model Derived From Rapid Response Team Activations. Journal of Patient Safety 2022;18(6):578 View
  11. Itelman E, Shlomai G, Leibowitz A, Weinstein S, Yakir M, Tamir I, Sagiv M, Muhsen A, Perelman M, Kant D, Zilber E, Segal G. Assessing the Usability of a Novel Wearable Remote Patient Monitoring Device for the Early Detection of In-Hospital Patient Deterioration: Observational Study. JMIR Formative Research 2022;6(6):e36066 View
  12. Brankovic A, Hassanzadeh H, Good N, Mann K, Khanna S, Abdel-Hafez A, Cook D. Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment. Scientific Reports 2022;12(1) View
  13. Gonem S, Taylor A, Figueredo G, Forster S, Quinlan P, Garibaldi J, McKeever T, Shaw D. Dynamic early warning scores for predicting clinical deterioration in patients with respiratory disease. Respiratory Research 2022;23(1) View
  14. Tennant R, Graham J, Mercer K, Ansermino J, Burns C. Automated digital technologies for supporting sepsis prediction in children: a scoping review protocol. BMJ Open 2022;12(11):e065429 View
  15. Blythe R, Parsons R, White N, Cook D, McPhail S. A scoping review of real-time automated clinical deterioration alerts and evidence of impacts on hospitalised patient outcomes. BMJ Quality & Safety 2022;31(10):725 View
  16. Petch J, Di S, Nelson W. Opening the Black Box: The Promise and Limitations of Explainable Machine Learning in Cardiology. Canadian Journal of Cardiology 2022;38(2):204 View
  17. Wan Y, Del Fiol G, McFarland M, Wright M. User interface approaches implemented with automated patient deterioration surveillance tools: protocol for a scoping review. BMJ Open 2022;12(1):e055525 View
  18. Schwartz J, George M, Rossetti S, Dykes P, Minshall S, Lucas E, Cato K. Factors Influencing Clinician Trust in Predictive Clinical Decision Support Systems for In-Hospital Deterioration: Qualitative Descriptive Study. JMIR Human Factors 2022;9(2):e33960 View
  19. Baidillah M, Busono P, Riyanto R. Mechanical ventilation intervention based on machine learning from vital signs monitoring: a scoping review. Measurement Science and Technology 2023;34(6):062001 View
  20. Li J, Xi F, Yu W, Sun C, Wang X. Real-Time Prediction of Sepsis in Critical Trauma Patients: Machine Learning–Based Modeling Study. JMIR Formative Research 2023;7:e42452 View
  21. Kim J, Kim B, Kim M, Hyun H, Kim H, Chang H. Prediction of inappropriate pre-hospital transfer of patients with suspected cardiovascular emergency diseases using machine learning: a retrospective observational study. BMC Medical Informatics and Decision Making 2023;23(1) View
  22. Jahandideh S, Ozavci G, Sahle B, Kouzani A, Magrabi F, Bucknall T. Evaluation of machine learning-based models for prediction of clinical deterioration: A systematic literature review. International Journal of Medical Informatics 2023;175:105084 View
  23. Zoodsma R, Bosch R, Alderliesten T, Bollen C, Kappen T, Koomen E, Siebes A, Nijman J. Continuous Data-Driven Monitoring in Critical Congenital Heart Disease: Clinical Deterioration Model Development. JMIR Cardio 2023;7:e45190 View
  24. van den Eijnden M, van der Stam J, Bouwman R, Mestrom E, Verhaegh W, van Riel N, Cox L. Machine Learning for Postoperative Continuous Recovery Scores of Oncology Patients in Perioperative Care with Data from Wearables. Sensors 2023;23(9):4455 View
  25. Verma A, Pou-Prom C, McCoy L, Murray J, Nestor B, Bell S, Mourad O, Fralick M, Friedrich J, Ghassemi M, Mamdani M. Developing and Validating a Prediction Model For Death or Critical Illness in Hospitalized Adults, an Opportunity for Human-Computer Collaboration. Critical Care Explorations 2023;5(5):e0897 View
  26. Doshi S, Shin S, Lapointe-Shaw L, Fowler R, Fralick M, Kwan J, Shojania K, Tang T, Razak F, Verma A. Temporal Clustering of Critical Illness Events on Medical Wards. JAMA Internal Medicine 2023;183(9):924 View
  27. Wan Y, Wright M, McFarland M, Dishman D, Nies M, Rush A, Madaras-Kelly K, Jeppesen A, Del Fiol G. Information displays for automated surveillance algorithms of in-hospital patient deterioration: a scoping review. Journal of the American Medical Informatics Association 2023;31(1):256 View
  28. Charan G, Charan A, Khurana M, Narang G. Impact of Analytics Applying Artificial Intelligence and Machine Learning on Enhancing Intensive Care Unit: A Narrative Review. Galician Medical Journal 2023;30(4) View
  29. Choi A, Choi S, Chung K, Chung H, Song T, Choi B, Kim J. Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department. Scientific Reports 2023;13(1) View
  30. Asthana S, Prime S. The role of digital transformation in addressing health inequalities in coastal communities: barriers and enablers. Frontiers in Health Services 2023;3 View
  31. Rajanna A, Bellary V, Puranic S, C. N, Nagaraj J, A. E, K. P. Continuous Remote Monitoring in Moderate and Severe COVID-19 Patients. Cureus 2023 View
  32. van Rossum M, Bekhuis R, Wang Y, Hegeman J, Folbert E, Vollenbroek-Hutten M, Kalkman C, Kouwenhoven E, Hermens H. Early Warning Scores to Support Continuous Wireless Vital Sign Monitoring for Complication Prediction in Patients on Surgical Wards: Retrospective Observational Study. JMIR Perioperative Medicine 2023;6:e44483 View
  33. Byrd T, Southwell B, Ravishankar A, Tran T, Kc A, Phelan T, Melton-Meaux G, Usher M, Scheppmann D, Switzer S, Simon G, Tignanelli C. Validation of a Proprietary Deterioration Index Model and Performance in Hospitalized Adults. JAMA Network Open 2023;6(7):e2324176 View
  34. 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
  35. Salehinejad H, Meehan A, Rahman P, Core M, Borah B, Caraballo P. Novel machine learning model to improve performance of an early warning system in hospitalized patients: a retrospective multisite cross-validation study. eClinicalMedicine 2023;66:102312 View
  36. Stuijt D, Radanovic I, Kos M, Schoones J, Stuurman F, Exadaktylos V, Bins A, Bosch J, van Oijen M. Smartphone-Based Passive Sensing in Monitoring Patients With Cancer: A Systematic Review. JCO Clinical Cancer Informatics 2023;(7) View
  37. De Sario Velasquez G, Forte A, McLeod C, Bruce C, Pacheco-Spann L, Maita K, Avila F, Torres-Guzman R, Garcia J, Borna S, Felton C, Carter R, Haider C. Predicting Cardiopulmonary Arrest with Digital Biomarkers: A Systematic Review. Journal of Clinical Medicine 2023;12(23):7430 View
  38. Feinstein M, Katz D, Demaria S, Hofer I. Remote Monitoring and Artificial Intelligence: Outlook for 2050. Anesthesia & Analgesia 2024;138(2):350 View
  39. Su L, Liu S, Long Y, Chen C, Chen K, Chen M, Chen Y, Cheng Y, Cui Y, Ding Q, Ding R, Duan M, Gao T, Gu X, He H, He J, Hu B, Hu C, Huang R, Huang X, Jiang H, Jiang J, Lan Y, Li J, Li L, Li L, Li W, Li Y, Lin J, Luo X, Lyu F, Mao Z, Miao H, Shang X, Shang X, Shang Y, Shen Y, Shi Y, Sun Q, Sun W, Tang Z, Wang B, Wang H, Wang H, Wang L, Wang L, Wang S, Wang Z, Wang Z, Wei D, Wu J, Wu Q, Xing X, Yang J, Yang X, Yu J, Yu W, Yu Y, Yuan H, Zhai Q, Zhang H, Zhang L, Zhang M, Zhang Z, Zhao C, Zheng R, Zhong L, Zhou F, Zhu W. Chinese experts’ consensus on the application of intensive care big data. Frontiers in Medicine 2024;10 View
  40. Yilmaz Y. Stacked ensemble modeling for improved tuberculosis treatment outcome prediction in pediatric cases. Concurrency and Computation: Practice and Experience 2024;36(13) View
  41. Armoundas A, Narayan S, Arnett D, Spector-Bagdady K, Bennett D, Celi L, Friedman P, Gollob M, Hall J, Kwitek A, Lett E, Menon B, Sheehan K, Al-Zaiti S. Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association. Circulation 2024;149(14) View
  42. Henry K, Giannini H. Early Warning Systems for Critical Illness Outside the Intensive Care Unit. Critical Care Clinics 2024;40(3):561 View
  43. Weissman G. Moving From In Silico to In Clinico Evaluations of Machine Learning-Based Interventions in Critical Care*. Critical Care Medicine 2024;52(7):1141 View
  44. Blythe R, Parsons R, Barnett A, Cook D, McPhail S, White N. Prioritising deteriorating patients using time-to-event analysis: prediction model development and internal–external validation. Critical Care 2024;28(1) View
  45. Davis H, Tseng S, Chua W. Oncology Intensive Care Units: Distinguishing Features and Clinical Considerations. Journal of Intensive Care Medicine 2024 View
  46. Blythe R, Naicker S, White N, Donovan R, Scott I, McKelliget A, McPhail S. Clinician perspectives and recommendations regarding design of clinical prediction models for deteriorating patients in acute care. BMC Medical Informatics and Decision Making 2024;24(1) View
  47. Shashikumar S, Le J, Yung N, Ford J, Singh K, Malhotra A, Nemati S, Wardi G. Development and Validation of a Deep Learning Model for Prediction of Adult Physiological Deterioration. Critical Care Explorations 2024;6(9):e1151 View
  48. Alcoceba-Herrero I, Coco-Martín M, Jiménez-Pérez J, Leal-Vega L, Martín-Gutiérrez A, Dueñas-Gutiérrez C, Miramontes-González J, Corral-Gudino L, de Castro-Rodríguez F, Royuela-Ruiz P, Arenillas-Lara J. Randomized Controlled Trial to Assess the Feasibility of a Novel Clinical Decision Support System Based on the Automatic Generation of Alerts through Remote Patient Monitoring. Journal of Clinical Medicine 2024;13(19):5974 View
  49. Verma A. Toward the Rigorous Evaluation of Early Warning Scores. JAMA Network Open 2024;7(10):e2438966 View
  50. Patel A, Maruthananth K, Matharu N, Pinto A, Hosseini B. Early Warning Systems for Acute Respiratory Infections: Scoping Review of Global Evidence. JMIR Public Health and Surveillance 2024;10:e62641 View
  51. Kim G, Lee S, Kim S, Han K, Lee S, Song J, Lee H. Development of continuous warning system for timely prediction of septic shock. Frontiers in Physiology 2024;15 View
  52. Tennant R, Graham J, Kern J, Mercer K, Ansermino J, Burns C. A scoping review on pediatric sepsis prediction technologies in healthcare. npj Digital Medicine 2024;7(1) View

Books/Policy Documents

  1. Shamout F. Digital Health. View
  2. de Souza A, Ferreira F, Lambrecht R, Reichow L, Santos H, Reiser R, Yamin A. Intelligent Systems. View
  3. Vasileiou Z, Meditskos G, Vrochidis S, Bassiliades N. Database and Expert Systems Applications - DEXA 2022 Workshops. View
  4. Jagirdar S, Vakulabharanam V, Prasad G S, Bejugama A. Explainable AI in Health Informatics. View
  5. Pulipeti S, Chithaluru P, Kumar M, Narsimhulu P, V U. Explainable AI in Health Informatics. View
  6. Anitha D, Sasikala S, Velmurugan A, Sharmila V, Banupriya R, Muthusamy P. Social Innovations in Education, Environment, and Healthcare. View
  7. Papadopoulou P, Apostolaki S, Lytras M, Konstantinopoulou S. Policies, Initiatives, and Innovations for Global Health. View