Development and Validation of an Interpretable Conformal Predictor to Predict Sepsis Mortality Risk: Retrospective Cohort Study.

ABSTRACT:

Background

Early and reliable identification of patients with sepsis who are at high risk of mortality is important to improve clinical outcomes. However, 3 major barriers to artificial intelligence (AI) models, including the lack of interpretability, the difficulty in generalizability, and the risk of automation bias, hinder the widespread adoption of AI models for use in clinical practice.

Objective

This study aimed to develop and validate (internally and externally) a conformal predictor of sepsis mortality risk in patients who are critically ill, leveraging AI-assisted prediction modeling. The proposed approach enables explaining the model output and assessing its confidence level.

Methods

We retrospectively extracted data on adult patients with sepsis from a data

SUBMITTER: Yang M 

PROVIDER: S-EPMC10985608 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature