Predicting Clostridioides difficile infection outcomes with explainable machine learning.

ABSTRACT:

Background

Clostridioides difficile infection results in life-threatening short-term outcomes and the potential for subsequent recurrent infection. Predicting these outcomes at diagnosis, when important clinical decisions need to be made, has proven to be a difficult task.

Methods

52 clinical features from existing models or the literature were collected retrospectively within ±48 h of diagnosis among 1660 inpatient infections. A modified desirability of outcome ranking (DOOR) was designed to encompass clinically-important severe events attributable to the acute infection (intensive care transfer due to sepsis, shock, colectomy/ileostomy, mortality) and/or 60-day recurrence. A deep neural network was constructed and interpreted using SHapley Additive exPlanations (SHAP). Hig

SUBMITTER: Madden GR 

PROVIDER: S-EPMC11286990 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature