Machine-learning-derived sepsis bundle of care

被引:0
作者
Alexandre Kalimouttou
Ivan Lerner
Chérifa Cheurfa
Anne-Sophie Jannot
Romain Pirracchio
机构
[1] Université Paris Cité and Université Sorbonne Paris Nord,Inserm UMR 1153, Center of Research in Epidemiology and StatisticS (CRESS), ECSTRRA Team
[2] Assistance Publique Hôpitaux de Paris-Centre (AP-HP),Department of Medical Informatics
[3] Georges Pompidou European Hospital,Inserm, Centre de Recherche des Cordeliers
[4] Sorbonne Université,Intensive Care Department, Assistance Publique Hôpitaux de Paris
[5] Université Paris Cité,Centre (AP
[6] HeKA,HP), Cochin Hospital
[7] Inria Paris,Department of Anesthesia and Perioperative Medicine
[8] Université Paris Cité,undefined
[9] Zuckerberg San Francisco General Hospital and Trauma Center,undefined
[10] University of California San Francisco,undefined
来源
Intensive Care Medicine | 2023年 / 49卷
关键词
Sepsis; Septic shock; Machine learning; Guidelines; Compliance;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
页码:26 / 36
页数:10
相关论文
共 50 条
  • [21] Machine Learning and Sepsis: On the Road to Revolution
    Liu, Vincent X.
    Walkey, Allan J.
    CRITICAL CARE MEDICINE, 2017, 45 (11) : 1946 - 1947
  • [22] Enhancing sepsis management through machine learning techniques: A review
    Ocampo-Quintero, N.
    Vidal-Cortes, P.
    del Rio Carbajo, L.
    Fdez-Riverola, F.
    Reboiro-Jato, M.
    Glez-Pena, D.
    MEDICINA INTENSIVA, 2022, 46 (03) : 140 - 156
  • [23] Early Detection of Sepsis Induced Deterioration Using Machine Learning
    Dal Canton, Francesco
    Quinten, Vincent M.
    Wiering, Marco A.
    ARTIFICIAL INTELLIGENCE, BNAIC 2018, 2019, 1021 : 1 - 15
  • [24] On classifying sepsis heterogeneity in the ICU: insight using machine learning
    Ibrahim, Zina M.
    Wu, Honghan
    Hamoud, Ahmed
    Stappen, Lukas
    Dobson, Richard J. B.
    Agarossi, Andrea
    JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2020, 27 (03) : 437 - 443
  • [25] Machine-learning models for prediction of sepsis patients mortality
    Bao, C.
    Deng, F.
    Zhao, S.
    MEDICINA INTENSIVA, 2023, 47 (06) : 315 - 325
  • [26] Machine learning for predicting acute myocardial infarction in patients with sepsis
    Fang, Shusheng
    Jin, Hongjin
    Zhang, Jiayu
    Wang, Yali
    Nan, Wenyu
    Feng, Yanhao
    Su, Wen
    Jiang, Xiaodong
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [27] A machine-learning-derived online prediction model for depression risk in COPD patients: A retrospective cohort study from CHARLS
    Zhao, Xuanna
    Wang, Yunan
    Li, Jiahua
    Liu, Weiliang
    Yang, Yuting
    Qiao, Youping
    Liao, Jinyu
    Chen, Min
    Li, Dongming
    Wu, Bin
    Huang, Dan
    Wu, Dong
    JOURNAL OF AFFECTIVE DISORDERS, 2025, 377 : 284 - 293
  • [28] Identifying the Risk of Sepsis in Patients With Cancer Using Digital Health Care Records: Machine Learning-Based Approach
    Yang, Donghun
    Kim, Jimin
    Yoo, Junsang
    Cha, Won Chul
    Paik, Hyojung
    JMIR MEDICAL INFORMATICS, 2022, 10 (06)
  • [29] The compliance of sepsis resuscitation care bundle in patients diagnosed with septic shock or sepsis to the International Surviving Sepsis Campaign among patients admitted to a tertiary and teaching hospital, Ethiopia: A 1-year prospective observational study
    Eyayu, Ruth Ayanaw
    Ashagrie, Henos Enyew
    Zeleke, Tadael Gudayu
    Chekol, Wubie Birlie
    Melesse, Debas Yaregal
    PERIOPERATIVE CARE AND OPERATING ROOM MANAGEMENT, 2024, 37
  • [30] Machine-learning-derived rules set excludes risk of Parkinson's disease in patients with olfactory or gustatory symptoms with high accuracy
    Loetsch, Joern
    Haehner, Antje
    Hummel, Thomas
    JOURNAL OF NEUROLOGY, 2020, 267 (02) : 469 - 478