Emergency department admissions during COVID-19: explainable machine learning to characterise data drift and detect emergent health risks

2021-05-29 emergency medicine Title + abstract only

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Supervised machine learning algorithms deployed in acute healthcare settings use data describing historical episodes to predict clinical outcomes. Clinical settings are dynamic environments and the underlying data distributions characterising episodes can change with time (a phenomenon known as data drift), and so can the relationship between episode characteristics and associated clinical outcomes (so-called, concept drift). We demonstrate how explainable machine learning can be used to monitor...

Emergency department admissions during COVID-19: explainable machine learning to characterise data drift and detect emergent health risks

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