Machine-learning based identification of undiagnosed dementia in primary care: a feasibility study

Topic: Risk, prevention and care in population studies

Background Up to half of patients with dementia may not receive a formal diagnosis, limiting access to appropriate services. It is hypothesised that it may be possible to identify undiagnosed dementia from a profile of symptoms recorded in routine clinical practice. Aim The aim of this study is to develop a machine learning-based model that could be used in general practice to detect dementia from routinely collected NHS data. The model would be a useful tool for identifying people who may be living with dementia but have not been formally diagnosed. Design & setting The study involved a case-control design and analysis of primary care data routinely collected over a 2-year period. Dementia diagnosed during the study period was compared to no diagnosis of dementia during the same period using pseudonymised routinely collected primary care clinical data. Method Routinely collected Read-encoded data were obtained from 18 consenting GP surgeries across Devon, for 26 483 patients aged >65 years. The authors determined Read codes assigned to patients that may contribute to dementia risk. These codes were used as features to train a machine-learning classification model to identify patients that may have underlying dementia. Results The model obtained sensitivity and specificity values of 84.47% and 86.67%, respectively. Conclusion The results show that routinely collected primary care data may be used to identify undiagnosed dementia. The methodology is promising and, if successfully developed and deployed, may help to increase dementia diagnosis in primary care.

@article{jammeh2018-30564722,
  author  = {Jammeh, E. A. and Carroll, C. B. and Pearson, S. W. and Escudero, J. and Anastasiou, A. and Zhao, P. and Chenore, T. and Zajicek, J. and others},
  title   = {{Machine-learning based identification of undiagnosed dementia in primary care: a feasibility study}},
  journal = {BJGP open},
  year    = {2018},
  doi     = {10.3399/bjgpopen18x101589},
  pmid    = {30564722},
  url     = {https://doi.org/10.3399/bjgpopen18x101589}
}

Key findings

• Dementia diagnosed during the study period was compared to no diagnosis of dementia during the same period using pseudonymised routinely collected primary care clinical data.
• Results The model obtained sensitivity and specificity values of 84.47% and 86.67%, respectively.
• Conclusion The results show that routinely collected primary care data may be used to identify undiagnosed dementia.

Why it matters

This case-control feasibility study used routinely collected primary care data from 26,483 patients aged over 65 to train a machine learning model to identify undiagnosed dementia, achieving sensitivity and specificity values of 84.47% and 86.67%. The results show that routinely collected primary care data may be used to identify undiagnosed dementia.

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Study details

• Study designcase-control study
• Participants483
• Effect size84.47%; 86.67%
• Evidence strengthModerate

Read the original

• DOI: 10.3399/bjgpopen18x101589
• PubMed: 30564722
• Original source →
• Free full text: BJGP Open →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• Missed and delayed diagnosis of dementia in primary care: prevalence and contributing factors Alzheimer disease and associated disorders · 2009-10-01 · usefulness score 67/100 · Getting a diagnosis
• Risk score for the prediction of dementia risk in 20 years among middle aged people: a longitudinal, population-based study The Lancet. Neurology · 2006-09-01 · usefulness score 62/100 · Prevention and risk
• Development and validation of a brief dementia screening indicator for primary care Alzheimer's & dementia : the journal of the Alzheimer's Association · 2014-02-01 · usefulness score 57/100 · Long-term cohort studies
• Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests BMC research notes · 2011-08-17 · usefulness score 47/100 · Digital health tools
• Machine-learning techniques for building a diagnostic model for very mild dementia NeuroImage · 2010-04-09 · usefulness score 44/100 · Digital health tools

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• Behavioural and psychological symptoms in general hospital patients with dementia, distress for nursing staff and complications in care: results of the General Hospital Study Epidemiology and psychiatric sciences · 2017-01-09 · usefulness score 55/100 · Clinical safety
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