Ford, Elizabeth, Starlinger, Johannes, Rooney, Philip, Oliver, Seb, Banerjee, Sube, van Marwijk, Harm and Cassell, Jackie (2020) Could dementia be detected from UK primary care patients’ records by simple automated methods earlier than by the treating physician? A retrospective case-control study. Wellcome Open Research, 5 (a120). pp. 1-14. ISSN 2398-502X

	Microsoft Excel (XSLX) (Appendix 1 - Dementia Code List) - Supplemental Material Download (20kB)
	Microsoft Excel (XSLX) (Appendix 2 - List of feature names) - Supplemental Material Download (10kB)
	Microsoft Excel (XSLX) (Appendix 3 - GP consultation full data) - Supplemental Material Download (18kB)
	PDF (Version 1) - Accepted Version Available under License Creative Commons Attribution. Download (688kB)
	PDF - Published Version Available under License Creative Commons Attribution. Download (654kB)

Abstract

Background: Timely diagnosis of dementia is a policy priority in the United Kingdom (UK). Primary care physicians receive incentives to diagnose dementia;, however, 33% of patients are still not receiving a diagnosis. We explored automating early detection of dementia using data from patients’ electronic health records (EHRs). We investigated: a) how early a machine-learning model could accurately identify dementia before the physician;, b) if models could be tuned for dementia subtype;, and c) what the best clinical features were for achieving detection.

Methods: Using EHRs from Clinical Practice Research Datalink in a case-control design, we selected patients aged >65y with a diagnosis of dementia recorded 2000-2012 (cases) and matched them 1:1 to controls; we also identified subsets of Alzheimer’s and vVascular dementia patients. Using 77 coded concepts recorded in the 5 years before diagnosis, we trained random forest classifiers, and evaluated models using Area Under the Receiver Operating Characteristic Curve (AUC). We examined models by: year prior to diagnosis, subtype, and the most important features contributing to classification.

Results: 95,202 patients (median age 83y; 64.8% female) were included (50% dementia cases). Classification of dementia cases and controls was poor 2-5 years prior to physician-recorded diagnosis (AUC range 0.55-0.65) but good in the year before (AUC: 0.84). Features indicating increasing cognitive and physical frailty dominated models 2-5 years before diagnosis; in the final year, initiation of the dementia diagnostic pathway (symptoms, screening and referral) explained the sudden increase in accuracy. No substantial differences were seen between all-cause dementia and subtypes.

Conclusions: Automated detection of dementia earlier than the treating physician may be problematic, if using only primary care data. Future work should investigate more complex modelling, benefits of linking multiple sources of healthcare data and monitoring devices, or contextualising the algorithm to those cases that the GP would need to investigate.

Item Type:	Article
Keywords:	Dementia, diagnosis, primary care, electronic patient records, early detection, machine learning.
Schools and Departments:	Brighton and Sussex Medical School > Primary Care and Public Health
Subjects:	R Medicine > R Medicine (General) > R858 Computer applications to medicine. Medical informatics R Medicine > RC Internal medicine > RC0321 Neurosciences. Biological psychiatry. Neuropsychiatry > RC0346 Neurology. Diseases of the nervous system Including speech disorders
Depositing User:	Tyrone Knight
Date Deposited:	15 May 2020 09:34
Last Modified:	02 Sep 2020 10:00
URI:	http://sro.sussex.ac.uk/id/eprint/91279

View download statistics for this item

📧 Request an update