Introduction: 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.
Methods: 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.
Results: For conditions investigated, the nearest method performed well in comparison with standard logistic regression.
Conclusions: Results indicate that it may be possible to use histories to identify 'similar' patients and thus to modulate future likelihoods of a condition occurring.
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| http://dx.doi.org/10.3233/SHTI200176 | DOI Listing |
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