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

页码：26 / 36

页数：10

共 50 条

[21] Machine learning in critical care: state-of-the-art and a sepsis case study [J].

Alfredo Vellido ;

Vicent Ribas ;

Carles Morales ;

Adolfo Ruiz Sanmartín ;

Juan Carlos Ruiz Rodríguez .

BioMedical Engineering OnLine, 17

[22] Machine Learning and Sepsis: On the Road to Revolution [J].

Liu, Vincent X. ;

Walkey, Allan J. .

CRITICAL CARE MEDICINE, 2017, 45 (11) :1946-1947

[23] Enhancing sepsis management through machine learning techniques: A review [J].

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

[24] Machine-learning models for prediction of sepsis patients mortality [J].

Bao, C. ;

Deng, F. ;

Zhao, S. .

MEDICINA INTENSIVA, 2023, 47 (06) :315-325

[25] Machine learning for predicting acute myocardial infarction in patients with sepsis [J].

Fang, Shusheng ;

Jin, Hongjin ;

Zhang, Jiayu ;

Wang, Yali ;

Nan, Wenyu ;

Feng, Yanhao ;

Su, Wen ;

Jiang, Xiaodong .

SCIENTIFIC REPORTS, 2024, 14 (01)

[26] On classifying sepsis heterogeneity in the ICU: insight using machine learning [J].

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

[27] Early Detection of Sepsis Induced Deterioration Using Machine Learning [J].

Dal Canton, Francesco ;

Quinten, Vincent M. ;

Wiering, Marco A. .

ARTIFICIAL INTELLIGENCE, BNAIC 2018, 2019, 1021 :1-15

[28] Identifying the Risk of Sepsis in Patients With Cancer Using Digital Health Care Records: Machine Learning-Based Approach [J].

Yang, Donghun ;

Kim, Jimin ;

Yoo, Junsang ;

Cha, Won Chul ;

Paik, Hyojung .

JMIR MEDICAL INFORMATICS, 2022, 10 (06)

[29] A machine-learning-derived online prediction model for depression risk in COPD patients: A retrospective cohort study from CHARLS [J].

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

[30] 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 [J].

Eyayu, Ruth Ayanaw ;

Ashagrie, Henos Enyew ;

Zeleke, Tadael Gudayu ;

Chekol, Wubie Birlie ;

Melesse, Debas Yaregal .

PERIOPERATIVE CARE AND OPERATING ROOM MANAGEMENT, 2024, 37

← 1 2 3 4 5 →