Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review.

Islam, Khandaker Reajul; Prithula, Johayra; Kumar, Jaya; Tan, Toh Leong; Reaz, Mamun Bin Ibne; Sumon, Md Shaheenur Islam; Chowdhury, Muhammad E H

Islam, Khandaker Reajul; Prithula, Johayra; Kumar, Jaya; Tan, Toh Leong; Reaz, Mamun Bin Ibne; Sumon, Md Shaheenur Islam; Chowdhury, Muhammad E H.

Afiliação

Islam KR; Department of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia.
Prithula J; Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka 1000, Bangladesh.
Kumar J; Department of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia.
Tan TL; Department of Emergency Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia.
Reaz MBI; Department of Electrical and Electronic Engineering, Independent University, Bangladesh Bashundhara, Dhaka 1229, Bangladesh.
Sumon MSI; Department of Biomedical Engineering, Military Institute of Science and Technology (MIST), Dhaka 1216, Bangladesh.
Chowdhury MEH; Department of Electrical Engineering, Qatar University, Doha 2713, Qatar.

J Clin Med ; 12(17)2023 Aug 30.

Article em En | MEDLINE | ID: mdl-37685724

Palavras-chave

deep learning; early prediction; electronic health record; emergency department (ED); intensive care unit (ICU); machine learning; sepsis

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies / Systematic_reviews Idioma: En Revista: J Clin Med Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Malásia País de publicação: Suíça

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