Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine.

ABSTRACT:

Objective

The change in performance of machine learning models over time as a result of temporal dataset shift is a barrier to machine learning-derived models facilitating decision-making in clinical practice. Our aim was to describe technical procedures used to preserve the performance of machine learning models in the presence of temporal dataset shifts.

Methods

Studies were included if they were fully published articles that used machine learning and implemented a procedure to mitigate the effects of temporal dataset shift in a clinical setting. We described how dataset shift was measured, the procedures used to preserve model performance, and their effects.

Results

Of 4,457 potentially relevant publications identified, 15 were included. The impact of temporal data

SUBMITTER: Guo LL 

PROVIDER: S-EPMC8410238 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature