Assessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction.

ABSTRACT:

Background

Data drift can negatively impact the performance of machine learning algorithms (MLAs) that were trained on historical data. As such, MLAs should be continuously monitored and tuned to overcome the systematic changes that occur in the distribution of data. In this paper, we study the extent of data drift and provide insights about its characteristics for sepsis onset prediction. This study will help elucidate the nature of data drift for prediction of sepsis and similar diseases. This may aid with the development of more effective patient monitoring systems that can stratify risk for dynamic disease states in hospitals.

Methods

We devise a series of simulations that measure the effects of data drift in patients with sepsis. We simulate multiple scenarios in which

SUBMITTER: Rahmani K 

PROVIDER: S-EPMC9196120 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature