Transferability and interpretability of the sepsis prediction models in the intensive care unit.

ABSTRACT:

Background

We aimed to develop an early warning system for real-time sepsis prediction in the ICU by machine learning methods, with tools for interpretative analysis of the predictions. In particular, we focus on the deployment of the system in a target medical center with small historical samples.

Methods

Light Gradient Boosting Machine (LightGBM) and multilayer perceptron (MLP) were trained on Medical Information Mart for Intensive Care (MIMIC-III) dataset and then finetuned on the private Historical Database of local Ruijin Hospital (HDRJH) using transfer learning technique. The Shapley Additive Explanations (SHAP) analysis was employed to characterize the feature importance in the prediction inference. Ultimately, the performance of the sepsis prediction system was furth

SUBMITTER: Chen Q 

PROVIDER: S-EPMC9798724 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature