JMIR Publications

ABSTRACT

Background:

Early and reliable identification of septic patients at high risk of mortality is important to improve clinical outcomes. However, three major barriers to artificial intelligence (AI) models, including the lack of interpretability, the difficulty in generalisability, 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 critically ill patients 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 septic patients from a database collected in a teaching hospital at Beth Israel Deaconess Medical Center (BIDMC) for model training and internal validation. A large multi-center critical care database from the Philips eICU Research Institute was used for external validation. A total of 103 clinical features were extracted from the first day after admission. We developed an AI model using gradient-boosting machines to predict the mortality risk of sepsis and Mondrian conformal prediction to estimate the prediction uncertainty. The Shapley additive explanation method was used to explain the model.

Results:

A total of 16746 (80%) patients from BIDMC were used to train the model. When tested on the internal validation population of 4187 (20%) patients, the model achieved an area under the receiver operating characteristic curve of 0.858, which was reduced to 0.800 when externally validated on 10362 patients from the Philips eICU database. At a specified confidence level of 90%, for the internal validation cohort, the percentage of error predictions (n=438) out of all predictions (n=4187) was 10.5%, with 1229 (29.4%) predictions requiring human review. In contrast, the AI model without conformal prediction made 1449 (34.6%) errors. When externally validated, more predictions (4004 [38.6%]) were flagged for human review due to inter-database heterogeneity. Nevertheless, the model still produced significantly lower error rates compared to the AI point predictions (1221 [11.8%] vs 4540 [43.8%]). The global feature importance of the model and the clinically relevant risk factors contributing to a single patient were examined to show how the risk arose.

Conclusions:

By combining model explanation and conformal prediction, AI-based systems can be better translated into medical practice for clinical decision making.