Explainable artificial intelligence model to predict acute critical illness from electronic health records

TL;DR

This paper introduces an explainable AI system for early detection of acute critical illness using electronic health records, providing both predictions and explanations to improve clinical trust and decision-making.

Contribution

The novel xAI-EWS system combines high predictive accuracy with interpretability, enabling clinicians to understand which EHR data influence each prediction.

Findings

Achieved high predictive performance comparable to existing models

Provided clear explanations linking predictions to specific EHR data

Enhanced potential for clinical adoption through interpretability

Abstract

We developed an explainable artificial intelligence (AI) early warning score (xAI-EWS) system for early detection of acute critical illness. While maintaining a high predictive performance, our system explains to the clinician on which relevant electronic health records (EHRs) data the prediction is grounded. Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Early clinical prediction is typically based on manually calculated screening metrics that simply weigh these parameters, such as Early Warning Scores (EWS). The predictive performance of EWSs yields a tradeoff between sensitivity and specificity that can lead to negative outcomes for the patient. Previous work on EHR-trained AI systems offers promising results with high levels of predictive performance in relation to the early, real-time prediction of acute critical illness. However, without insight into the complex decisions by such system, clinical translation is hindered. In this letter, we present our xAI-EWS system, which potentiates clinical translation by accompanying a prediction with information on the EHR data explaining it.