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Artificial intelligence sepsis prediction algorithm learns to say "I don't know".

Supreeth P Shashikumar1, Gabriel Wardi2,3, Atul Malhotra3

  • 1Division of Biomedical Informatics, University of California San Diego, San Diego, USA. spshashikumar@health.ucsd.edu.

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COMPOSER, a deep learning model, accurately predicts sepsis risk early. It flags unfamiliar patient data as indeterminate, reducing false alarms and improving sepsis identification for timely treatment.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Informatics
  • Machine Learning for Healthcare

Background:

  • Sepsis is a critical global health issue, causing significant morbidity and mortality.
  • Early sepsis detection is vital for initiating prompt, life-saving interventions.
  • Existing prediction models often struggle with data variability and false alarms.

Purpose of the Study:

  • To introduce COMPOSER, a deep learning model for early sepsis risk prediction.
  • To enhance sepsis prediction accuracy by identifying and flagging unfamiliar patient data.
  • To reduce false alarms in sepsis detection within intensive care units (ICU) and emergency departments (ED).

Main Methods:

  • Developed COMPOSER (COnformal Multidimensional Prediction Of SEpsis Risk), a deep learning model.
  • Trained and validated the model on six patient cohorts (515,720 patients) from two US healthcare systems.
  • Evaluated performance in a sequential prediction setting across ICU and ED settings.

Main Results:

  • COMPOSER demonstrated high predictive performance with consistently high Area Under the Curve (AUC) values (ICU: 0.925-0.953; ED: 0.938-0.945).
  • The model identified approximately 20% of non-septic and 8% of septic cases as indeterminate, effectively flagging unfamiliar data.
  • Early sepsis warnings were provided within clinically actionable timeframes (ICU: 12.2 hours; ED: 2.1 hours prior to antibiotics).

Conclusions:

  • COMPOSER offers a robust deep learning approach for early sepsis prediction.
  • The model's ability to flag indeterminate cases improves reliability and reduces spurious predictions.
  • COMPOSER facilitates timely identification and prioritization of high-risk sepsis patients, potentially improving outcomes.