Longitudinal Model Shifts of Machine Learning-Based Clinical Risk Prediction Models: Evaluation Study of Multiple Use Cases Across Different Hospitals.

ABSTRACT:

Background

In recent years, machine learning (ML)-based models have been widely used in clinical domains to predict clinical risk events. However, in production, the performances of such models heavily rely on changes in the system and data. The dynamic nature of the system environment, characterized by continuous changes, has significant implications for prediction models, leading to performance degradation and reduced clinical efficacy. Thus, monitoring model shifts and evaluating their impact on prediction models are of utmost importance.

Objective

This study aimed to assess the impact of a model shift on ML-based prediction models by evaluating 3 different use cases-delirium, sepsis, and acute kidney injury (AKI)-from 2 hospitals (M and H) with different patient populati

SUBMITTER: Cabanillas Silva P 

PROVIDER: S-EPMC11681292 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature