COVID vs. COVID (part 2)

Further investigation of differences in COVID diagnoses as derived from application of machine learning (MLP) and as recorded in the EPR of an unknown NHS Trust

In part 1 of this mini-series we took a quick look at how the COVID diagnosis as registered in the EPR of 19,457 in-hospital deaths stacked up against the predicted COVID diagnosis I have derived using machine learning applied to a matrix of symptoms, age, sex and disease prevalence. We observed a decent overlap of 86% of cases with just 14% falling into the ‘we-better-query-these’ category. This morning I’m going to follow through on the ‘we-better-query-these’ category by… er… running some queries, and I shall start by creating an indicator variable (COVagree) that identifies the 2,767 cases that were not in diagnostic agreement.

At this point I could sit with umpteen tables and a bucket of slides and work my way through trying to ascertain which, if any, factors stand out in distinguishing diagnostic agreement or I could rely on machine learning again and use multilayer perceptron to tell me in 6 seconds flat what it thinks is important. Herewith its final decision:

Case Complexity

Th…