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

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

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

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.

Integrity signals

Registry

Crossref · 10.1016/j.compbiomed.2017.08.015

Cites

47 of 47 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

5 of the 52 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

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 › Epidemiology › 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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review