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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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A Data-Driven Approach to Quantifying Immune States in Sepsis
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Machine Learning-Augmented Triage for Sepsis: Real-Time ICU Mortality Prediction Using SHAP-Explained Meta-Ensemble

Hülya Yilmaz Başer1, Turan Evran2, Mehmet Akif Cifci3,4

  • 1Department of Emergency Medicine, Faculty of Medicine, Bandirma Onyedi Eylul University, 10250 Balıkesir, Türkiye.

Biomedicines
|June 26, 2025
PubMed
Summary

This study introduces an interpretable AI framework for predicting sepsis mortality, outperforming traditional scoring systems. The AI model enhances clinical decision-making for early intervention and improved patient survival rates.

Keywords:
clinical critical decision supportdeep learningemergency departmentin-hospital mortalityintensive care unitmachine learningpredictive modelingsepsisstacked ensemble model

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Area of Science:

  • Artificial Intelligence in Medicine
  • Machine Learning for Healthcare
  • Computational Biology and Bioinformatics

Background:

  • Sepsis poses a significant mortality risk, especially in intensive care units.
  • Traditional scoring systems (qSOFA, SIRS, NEWS) lack precision for timely sepsis management.
  • Optimization algorithms and metaheuristics offer advanced solutions for complex problems in healthcare.

Purpose of the Study:

  • To develop a novel, interpretable machine learning framework for predicting in-hospital mortality in sepsis patients.
  • To improve upon the diagnostic accuracy of existing clinical scoring systems for sepsis.
  • To leverage bio-inspired optimization algorithms for enhanced predictive modeling in critical care.

Main Methods:

  • A retrospective dataset of sepsis patients was analyzed, incorporating clinical and laboratory features.
  • Synthetic Minority Oversampling Technique and imputation methods addressed data imbalance and missing values.
  • A hybrid model combining ensemble ML and deep learning, optimized by the Red Piranha Optimization algorithm, was developed and validated.

Main Results:

  • The proposed AI model achieved a high predictive performance with an area under the receiver operating characteristic curve of 0.96.
  • The model demonstrated a Brier score of 0.118 and a recall of 81%, indicating superior accuracy.
  • The AI framework significantly outperformed conventional scoring systems in predicting sepsis mortality.

Conclusions:

  • AI-driven tools show significant potential for enhancing clinical decision-making in sepsis management.
  • Early and accurate prediction of sepsis mortality can facilitate timely interventions.
  • This research highlights the value of advanced machine learning and optimization techniques in critical care settings.