Modulation of Medical Condition Likelihood by Patient History Similarity.

Abstract

We describe an analysis that modulates the simple population prevalence derived likelihood of a particular condition occurring in an individual by matching the individual with other individuals with similar clinical histories and determining the prevalence of the condition within the matched group. We have taken clinical event codes and dates from anonymised longitudinal primary care records for 25,979 patients with 749,053 recorded clinical events. Using a nearest neighbour approach, for each patient, the likelihood of a condition occurring was adjusted from the population prevalence to the prevalence of the condition within those patients with the closest matching clinical history. For conditions investigated, the nearest method performed well in comparison with standard logistic regression. Results indicate that it may be possible to use histories to identify 'similar' patients and thus to modulate future likelihoods of a condition occurring.