Author ORCID Identifier
https://orcid.org/0000-0003-2841-9738
Document Type
Article
Disciplines
Computer Sciences
Abstract
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.
DOI
https://doi.org/10.3233/shti200176
Recommended Citation
Turner J, O'Sullivan D, Bird J. Modulation of Medical Condition Likelihood by Patient History Similarity. Stud Health Technol Inform. 2020 Jun 16;270:327-331. doi: 10.3233/SHTI200176. PMID: 32570400.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Publication Details
Studies in Health Technology and Informatics