Leveraging explainable artificial intelligence for early prediction of bloodstream infections using historical electronic health records.

Bloodstream infections (BSIs) are a severe public health threat due to their rapid progression into critical conditions like sepsis. This study presents a novel eXplainable Artificial Intelligence (XAI) framework to predict BSIs using historical electronic health records (EHRs). Leveraging a dataset from St.

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

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.

Predicting In-Hospital Death from Derived EHR Trajectory Features.

Medical histories of patients can predict a patient's immediate future. While most studies propose to predict survival from vital signs and hospital tests within one episode of care, we carried out selective feature engineering from longitudinal medical records in this study to develop a dataset with derived features. We thereafter trained multiple machine learning models for the binary prediction of whether an episode of care will culminate in death among patients suspected of bloodstream infections.