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Identifying undetected dementia in UK primary care patients: a retrospective case-control study comparing machine-learning and standard epidemiological approaches
Version 2 2023-06-12, 09:15
Version 1 2023-06-09, 19:44
journal contribution
posted on 2023-06-12, 09:15 authored by Elizabeth FordElizabeth Ford, Philip Rooney, Seb OliverSeb Oliver, Richard Hoile, Pete Hurley, Sube Banerjee, Harm van MarwijkHarm van Marwijk, Jackie CassellBackground Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a formal diagnosis in United Kingdom health systems and many receive it late in the disease process, there is ample room for improvement. The policy of the UK government and National Health Service (NHS) is to increase rates of timely dementia diagnosis. We used data from general practice (GP) patient records to create a machine-learning model to identify patients who have or who are developing dementia, but are currently undetected as having the condition by the GP. Methods We used electronic patient records from Clinical Practice Research Datalink (CPRD). Using a case-control design, we selected patients aged >65y with a diagnosis of dementia (cases) and matched them 1:1 by sex and age to patients with no evidence of dementia (controls). We developed a list of 70 clinical entities related to the onset of dementia and recorded in the 5 years before diagnosis. After creating binary features, we trialled machine learning classifiers to discriminate between cases and controls (logistic regression, naïve Bayes, support vector machines, random forest and neural networks). We examined the most important features contributing to discrimination. Results The final analysis included data on 93,120 patients, with a median age of 82.6 years; 64.8% were female. The naïve Bayes model performed least well. The logistic regression, support vector machine, neural network and random forest performed very similarly with an AUROC of 0.74. The top features retained in the logistic regression model were disorientation and wandering, behaviour change, schizophrenia, self-neglect, and difficulty managing. Conclusions Our model could aid GPs or health service planners with the early detection of dementia. Future work could improve the model by exploring the longitudinal nature of patient data and modelling decline in function over time.
Funding
ASTRODEM: Using astrophysics to close the 'diagnosis gap' for dementia in UK general practice.; G1895; WELLCOME TRUST; 202133/Z/16/Z
History
Publication status
- Published
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- Published version
Journal
BMC Medical Informatics and Decision MakingISSN
1472-6947Publisher
BMCExternal DOI
Volume
19Article number
a248Department affiliated with
- Primary Care and Public Health Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2019-11-25First Open Access (FOA) Date
2019-11-25First Compliant Deposit (FCD) Date
2019-11-22Usage metrics
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