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Related Experiment Video

Updated: Jan 9, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
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Sepsis mortality prediction using machine learning and deep learning - a systematic review.

Mohannad N AbuHaweeleh1,2, Adiba Tabassum Chowdhury3, Mehrin Newaz3

  • 1Department of Basic Medical Sciences, College of Medicine, Qatar University, Doha, 2713, Qatar.

BMC Medical Informatics and Decision Making
|December 10, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning and deep learning show promise for predicting sepsis using electronic health records (EHR). Standardized methods are needed for reliable early detection and improved patient outcomes.

Keywords:
Clinical decision-makingDeep learningFeature extractionMachine learningModel interpretabilityPredictive analyticsReal-time monitoringSepsis mortality prediction

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Computational Biology

Background:

  • Sepsis is a life-threatening inflammatory condition requiring prompt diagnosis.
  • Electronic Health Records (EHR) offer vast data for sepsis prediction.
  • Machine Learning (ML) and Deep Learning (DL) are emerging tools for healthcare.

Purpose of the Study:

  • To systematically review the application of ML/DL in sepsis prediction using EHR data.
  • To identify trends, challenges, and future directions in this field.

Main Methods:

  • Comprehensive literature search across major scientific databases (PubMed, IEEE Xplore, Google Scholar, Scopus).
  • Inclusion criteria applied to 39 selected studies.
  • Analysis of study characteristics, methodologies, and reported outcomes.

Main Results:

  • The majority of studies (34/39) were retrospective and geographically diverse.
  • Significant heterogeneity observed in datasets, sepsis definitions, model parameters, and quality assessments.
  • Longitudinal EHR data demonstrated potential for early sepsis prediction despite variations.

Conclusions:

  • ML/DL methodologies hold significant promise for early sepsis detection and prediction using EHR.
  • Standardized evaluation metrics and quality assessments are crucial for advancing the field.
  • Addressing data heterogeneity and funding disparities is essential for reliable implementation.