Development of an artificial intelligence bacteremia prediction model and evaluation of its impact on physician predictions focusing on uncertainty.

ABSTRACT: Prediction of bacteremia is a clinically important but challenging task. An artificial intelligence (AI) model has the potential to facilitate early bacteremia prediction, aiding emergency department (ED) physicians in making timely decisions and reducing unnecessary medical costs. In this study, we developed and externally validated a Bayesian neural network-based AI bacteremia prediction model (AI-BPM). We also evaluated its impact on physician predictive performance considering both AI and physician uncertainties using historical patient data. A retrospective cohort of 15,362 adult patients with blood cultures performed in the ED was used to develop the AI-BPM. The AI-BPM used structured and unstructured text data acquired during the early stage of ED visit, and provided both the point

SUBMITTER: Choi DH 

PROVIDER: S-EPMC10439897 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature