دورية أكاديمية
Moving From In Silico to In Clinico Evaluations of Machine Learning-Based Interventions in Critical Care.
العنوان: | Moving From In Silico to In Clinico Evaluations of Machine Learning-Based Interventions in Critical Care. |
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المؤلفون: | Weissman GE; Palliative and Advanced Illness Research (PAIR) Center, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.; Pulmonary, Allergy, Critical Care Division, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.; Division of Informatics, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.; Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA.; Penn Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA. |
المصدر: | Critical care medicine [Crit Care Med] 2024 Jul 01; Vol. 52 (7), pp. 1141-1144. Date of Electronic Publication: 2024 Jun 13. |
نوع المنشور: | Journal Article |
اللغة: | English |
بيانات الدورية: | Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 0355501 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1530-0293 (Electronic) Linking ISSN: 00903493 NLM ISO Abbreviation: Crit Care Med Subsets: MEDLINE |
أسماء مطبوعة: | Publication: Philadelphia, PA : Lippincott Williams & Wilkins Original Publication: New York, Kolen. |
مواضيع طبية MeSH: | Machine Learning* , Critical Care*/methods, Humans ; Computer Simulation |
مستخلص: | Competing Interests: Dr. Weissman has disclosed that he does not have any potential conflicts of interest. |
References: | Churpek MM, Yuen TC, Edelson DP: Predicting clinical deterioration in the hospital: The impact of outcome selection. Resuscitation. 2013; 84:564–568. Churpek MM, Yuen TC, Winslow C, et al.: Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards. Crit Care Med. 2016; 44:368–374. Muralitharan S, Nelson W, Di S, et al.: Machine learning–based early warning systems for clinical deterioration: Systematic scoping review. J Med Internet Res. 2021; 23:e25187. Volchenboum SL, Mayampurath A, GöksuGürsoy G, et al.: Association between inhospital critical illness events and outcomes in patients on the same ward. JAMA. 2016; 316:2674–2675. Levin MA, Kia A, Timsina P, et al.: Real-Time Machine Learning Alerts to Prevent Escalation of Care: A Nonrandomized Clustered Pragmatic Clinical Trial. Crit Care Med. 2024; 52:1007–1020. Kia A, Timsina P, Joshi HN, et al.: MEWS++: Enhancing the prediction of clinical deterioration in admitted patients through a machine learning model. J Clin Med. 2020; 9:343. van der Vegt AH, Campbell V, Mitchell I, et al.: Systematic review and longitudinal analysis of implementing artificial intelligence to predict clinical deterioration in adult hospitals: What is known and what remains uncertain. J Am Med Inform Assoc. 2024; 31:509–524. Orkin AM, Gill PJ, Ghersi D, et al.; CONSERVE Group: Guidelines for reporting trial protocols and completed trials modified due to the COVID19 pandemic and other extenuating circumstances: The CONSERVE 2021 statement. JAMA. 2021; 326:257–265. Downing NL, Rolnick J, Poole SF, et al.: Electronic health recordbased clinical decision support alert for severe sepsis: A randomised evaluation. BMJ Qual Saf. 2019; 28:762–768. Mansournia MA, Altman DG: Inverse probability weighting. BMJ. 2016; 352:i189. Benedetto U, Head SJ, Angelini GD, et al.: Statistical primer: Propensity score matching and its alternatives\dag. Eur J Cardio Thorac Surg. 2018; 53:1112–1117. Altman DG, Bland JM: Treatment allocation in controlled trials: Why randomise? BMJ. 1999; 318:1209. Nong P, Hamasha R, Singh K, et al.: How academic medical centers govern AI prediction tools in the context of uncertainty and evolving regulation. NEJM AI. 2024; 1:AIp2300048. Lee JT, Moffett AT, Maliha G, et al.: Analysis of devices authorized by the FDA for clinical decision support in critical care. JAMA Intern Med. 2023; 183:1399–1401. Mitchell OJL, Dewan M, Wolfe HA, et al.: Defining physiological decompensation: An expert consensus and retrospective outcome validation. Crit Care Explor. 2022; 4:e677. |
تواريخ الأحداث: | Date Created: 20240613 Date Completed: 20240613 Latest Revision: 20240613 |
رمز التحديث: | 20240613 |
DOI: | 10.1097/CCM.0000000000006277 |
PMID: | 38869387 |
قاعدة البيانات: | MEDLINE |
تدمد: | 1530-0293 |
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DOI: | 10.1097/CCM.0000000000006277 |