Cited by 218Sepsis Diagnosis and TreatmentMachine Learning in HealthcareArtificial Intelligence in Healthcare

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Learning representations for the early detection of sepsis with deep neural networks

Kam, Hye Jin, Kim, Ha Young·2017·Computers in Biology and Medicine

DOI 10.1016/j.compbiomed.2017.08.015

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Abstract

Background Sepsis is one of the leading causes of death in intensive care unit patients. Early detection of sepsis is vital because mortality increases as the sepsis stage worsens. Objective This study aimed to develop detection models for the early stage of sepsis using deep learning methodologies, and to compare the feasibility and performance of the new deep learning methodology with those of the regression method with conventional temporal feature extraction. Method Study group selection adhered to the InSight model. The results of the deep learning-based models and the InSight model were compared. Results With deep feedforward networks, the area under the ROC curve (AUC) of the models were 0.887 and 0.915 for the InSight and the new feature sets, respectively. For the model with the combined feature set, the AUC was the same as that of the basic feature set (0.915). For the long short-term memory model, only the basic feature set was applied and the AUC improved to 0.929 compared with the existing 0.887 of the InSight model. Conclusions The contributions of this paper can be summarized in three ways: (i) improved performance without feature extraction using domain knowledge, (ii) verification of feature extraction capability of deep neural networks through comparison with reference features, and (iii) improved performance with feedforward neural networks using long short-term memory, a neural network architecture that can learn sequential patterns.

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Authors: Kam, H. J., & Kim, H. Y.
Affiliations: Asan Medical Center (KR)
Ajou University (KR)
Corresponding author: Kim, Ha Young
ORCID: Kam, Hye Jin
Kim, Ha Young
Venue: Computers in Biology and Medicine
Publisher: Elsevier BV
Published: 2017-10
Volume: 89
Pages: 248-255
ISSN: 0010-4825 (ISSN-L)
DOI: 10.1016/j.compbiomed.2017.08.015
OpenAlex: W2750557731
Cited by: 218
References: 52
Citations by year: 2026: 10 · 2025: 22 · 2024: 21 · 2023: 28 · 2022: 29 · 2021: 41 · 2020: 31 · 2019: 26 · 2018: 9 · 2017: 1
FWCI: 14.43
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Computers in Biology and Medicine (publishedVersion)
PubMed (publishedVersion)
Funders: Ajou University
Korea Health Technology R&D Project
Korea Health Industry Development Institute
Ministry of Health & Welfare, Republic of Korea (HI16C0982)
Topics: Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Sepsis Diagnosis and Treatment
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: sepsis, early detection, deep neural network, long short-term memory, feedforward neural networks, feature extraction, intensive care unit patients, temporal feature extraction, sepsis mortality
MeSH terms: Machine Learning, Adult, Critical Care, Female, Humans, Male, Models, Biological, Neural Networks, Computer, Sepsis

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Learning representations for the early detection of sepsis with deep neural networks

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