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Updated: Dec 20, 2025

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Enhancing sepsis management through machine learning techniques: A review.

N Ocampo-Quintero1, P Vidal-Cortés2, L Del Río Carbajo2

  • 1ESEI - Escuela Superior de Ingeniería Informática, Universidad de Vigo, Ourense, Spain.

Medicina Intensiva
|June 3, 2020
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) can enhance sepsis management by improving early treatment and adherence to clinical guidelines. This review explores ML techniques for better sepsis prediction and patient outcomes.

Keywords:
Aprendizaje automáticoArtificial intelligenceClinical decision support systemsInteligencia artificialMachine learningSepsisSistemas de apoyo a la decisión clínica

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support Systems

Background:

  • Sepsis is a leading global cause of mortality.
  • Delayed treatment and guideline non-adherence increase sepsis mortality.
  • Clinical Decision Support Systems (CDSS) show promise in improving patient care.

Purpose of the Study:

  • To review Machine Learning (ML) applications in sepsis management.
  • To discuss ML techniques for improving early diagnosis and treatment.
  • To evaluate ML's impact on clinical outcomes and system accuracy.

Main Methods:

  • Narrative review of Machine Learning techniques in sepsis.
  • Analysis of ML methods for prediction and clinical decision support.
  • Synthesis of results regarding system performance and patient outcomes.

Main Results:

  • ML demonstrates significant potential in predicting sepsis and supporting clinical decisions.
  • Various ML techniques are applied to different aspects of sepsis management.
  • Studies show improvements in both intelligent system accuracy and clinical outcomes.

Conclusions:

  • Machine Learning offers powerful tools to enhance sepsis management strategies.
  • Adoption of ML in CDSS can lead to better patient outcomes and reduced mortality.
  • Further research into ML applications is crucial for advancing sepsis care.