In-hospital mortality, readmission, and prolonged length of stay risk prediction leveraging historical electronic patient records.

ABSTRACT:

Objective

This study aimed to investigate the predictive capabilities of historical patient records to predict patient adverse outcomes such as mortality, readmission, and prolonged length of stay (PLOS).

Methods

Leveraging a de-identified dataset from a tertiary care university hospital, we developed an eXplainable Artificial Intelligence (XAI) framework combining tree-based and traditional machine learning (ML) models with interpretations and statistical analysis of predictors of mortality, readmission, and PLOS.

Results

Our framework demonstrated exceptional predictive performance with a notable area under the receiver operating characteristic (AUROC) of 0.9625 and an area under the precision-recall curve (AUPRC) of 0.8575 for 30-day mortality at discharge and an A

SUBMITTER: Bopche R 

PROVIDER: S-EPMC11401612 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature