Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19.

ABSTRACT: A key task of emergency departments is to promptly identify patients who require hospital admission. Early identification ensures patient safety and aids organisational planning. Supervised machine learning algorithms can use data describing historical episodes to make ahead-of-time predictions of clinical outcomes. Despite this, clinical settings are dynamic environments and the underlying data distributions characterising episodes can change with time (data drift), and so can the relationship between episode characteristics and associated clinical outcomes (concept drift). Practically this means deployed algorithms must be monitored to ensure their safety. We demonstrate how explainable machine learning can be used to monitor data drift, using the COVID-19 pandemic as a severe example. W

SUBMITTER: Duckworth C 

PROVIDER: S-EPMC8626460 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature