Artificial Intelligence for Clinical Decision Support in Sepsis

被引：30

作者：

Wu, Miao ^{[1
]}

Du, Xianjin ^{[2
]}

Gu, Raymond ^{[3
]}

Wei, Jie ^{[1
]}

机构：

[1] Wuhan Univ, Dept Emergency, Renmin Hosp, Wuhan, Peoples R China

[2] Wuhan Univ, Dept Crit Care Med, Renmin Hosp, Wuhan, Peoples R China

[3] SUNY Upstate Med Univ, Dept Surg, Syracuse, NY 13210 USA

来源：

FRONTIERS IN MEDICINE | 2021年 / 8卷

基金：

中国国家自然科学基金;

关键词：

sepsis; artificial intelligence; machine learning; deep learning; early prediction; ELECTRONIC HEALTH RECORD; SEPTIC SHOCK; MACHINE; PREDICTION; MANAGEMENT; DIAGNOSIS; ICU;

D O I：

10.3389/fmed.2021.665464

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

Sepsis is one of the main causes of death in critically ill patients. Despite the continuous development of medical technology in recent years, its morbidity and mortality are still high. This is mainly related to the delay in starting treatment and non-adherence of clinical guidelines. Artificial intelligence (AI) is an evolving field in medicine, which has been used to develop a variety of innovative Clinical Decision Support Systems. It has shown great potential in predicting the clinical condition of patients and assisting in clinical decision-making. AI-derived algorithms can be applied to multiple stages of sepsis, such as early prediction, prognosis assessment, mortality prediction, and optimal management. This review describes the latest literature on AI for clinical decision support in sepsis, and outlines the application of AI in the prediction, diagnosis, subphenotyping, prognosis assessment, and clinical management of sepsis. In addition, we discussed the challenges of implementing and accepting this non-traditional methodology for clinical purposes.

引用

页数：9

共 50 条

[11]

Gonçalves LS, 2020, REV BRAS ENFERM, V73

[12] Artificial Intelligence in the Intensive Care Unit [J].

Greco, Massimiliano ;

Caruso, Pier F. ;

Cecconi, Maurizio .

SEMINARS IN RESPIRATORY AND CRITICAL CARE MEDICINE, 2021, 42 (01) :2-9

[13] Not All Sepsis-Associated Acute Kidney Injury Is the Same There May Be an App for That [J].

Gunning, Samantha ;

Koyner, Jay L. .

CLINICAL JOURNAL OF THE AMERICAN SOCIETY OF NEPHROLOGY, 2020, 15 (11) :1543-1545

[14] Artificial intelligence in health care: accountability and safety [J].

Habli, Ibrahim ;

Lawton, Tom ;

Porter, Zoe .

BULLETIN OF THE WORLD HEALTH ORGANIZATION, 2020, 98 (04) :251-256

[15] Comparative Analysis of Three Machine-Learning Techniques and Conventional Techniques for Predicting Sepsis-Induced Coagulopathy Progression [J].

Hasegawa, Daisuke ;

Yamakawa, Kazuma ;

Nishida, Kazuki ;

Okada, Naoki ;

Murao, Shuhei ;

Nishida, Osamu .

JOURNAL OF CLINICAL MEDICINE, 2020, 9 (07) :1-10

[16] Early Sepsis Prediction Using Ensemble Learning With Deep Features and Artificial Features Extracted From Clinical Electronic Health Records [J].

He, Zhengling ;

Du, Lidong ;

Zhang, Pengfei ;

Zhao, Rongjian ;

Chen, Xianxiang ;

Fang, Zhen .

CRITICAL CARE MEDICINE, 2020, 48 (12) :E1337-E1342

[17] Sepsis in the critically ill patient: current and emerging management strategies [J].

Heming, Nicholas ;

Azabou, Eric ;

Cazaumayou, Xavier ;

Moine, Pierre ;

Annane, Djillali .

EXPERT REVIEW OF ANTI-INFECTIVE THERAPY, 2021, 19 (05) :635-647

[18] 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

[19] An attention based deep learning model of clinical events in the intensive care unit [J].

Kaji, Deepak A. ;

Zech, John R. ;

Kim, Jun S. ;

Cho, Samuel K. ;

Dangayach, Neha S. ;

Costa, Anthony B. ;

Oermann, Eric K. .

PLOS ONE, 2019, 14 (02)

[20] Learning representations for the early detection of sepsis with deep neural networks [J].

Kam, Hye Jin ;

Kim, Ha Young .

COMPUTERS IN BIOLOGY AND MEDICINE, 2017, 89 :248-255

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