The DOI matches its Crossref record; 47 of the 52 references it cites were found in a registry or an index.
Learning representations for the early detection of sepsis with deep neural networks
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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“numerous models were tested on the freely accessible MIMIC database [27, 33, 52–59]”
From Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy (2020) · cites this paper as [53]
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Microbial traits have minimal power for predicting death from bloodstream infectionPreprint
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Metadata
- Authors
- Kam, H. J., & Kim, H. Y.
- Affiliations
- Asan Medical Center (KR)
- Ajou University (KR)
- Corresponding author
- 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)
- 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
- 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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