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Predicting patient deterioration with physiological data using AI: systematic review protocol.

Lynsey Threlfall1,2, Cen Cong3, Victoria Riccalton3

  • 1Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.

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|August 5, 2025
PubMed
Summary
This summary is machine-generated.

This systematic review identifies the best artificial intelligence (AI) algorithms for analyzing physiological data to predict patient deterioration. The goal is to improve upon the National Early Warning Score

Keywords:
Artificial intelligenceEmergency Service, HospitalHospital RecordsMachine Learning

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Area of Science:

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

Background:

  • The National Early Warning Score (NEWS2) is widely used but has limited predictive accuracy for patient deterioration.
  • Artificial intelligence (AI) shows promise in predicting clinical decline, yet optimal algorithms for physiological data analysis remain unclear.

Purpose of the Study:

  • To systematically review and identify the most effective AI and machine learning algorithms for analyzing physiological data to predict patient deterioration in hospital settings.

Main Methods:

  • A systematic review adhering to PRISMA and PICOS frameworks.
  • Comprehensive search of eight major databases (PubMed, Embase, CINAHL, Cochrane, Web of Science, Scopus, IEEE Xplore, ACM Digital Library) from 2007 to present.
  • Independent data screening and extraction by two reviewers, with discrepancies resolved by discussion.

Main Results:

  • This section will detail the findings of the systematic review, highlighting the performance of various AI/machine learning algorithms in predicting patient deterioration based on physiological data.
  • The review will compare the efficacy of different algorithms in analyzing complex physiological datasets.

Conclusions:

  • The findings will guide the selection of optimal AI algorithms for enhancing early detection of patient deterioration.
  • This research aims to improve clinical decision-making and patient outcomes by leveraging advanced AI in healthcare.