ArticleBJGP open2018
Machine-learning based identification of undiagnosed dementia in primary care: a feasibility study.
Article in BJGP open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic Prediction Models for Primary Care, Based on AI and Electronic Health Records: Systematic Review.JMIR medical informatics · 2025Pooled it
- Pooled it
- Machine learning-assisted screening for cognitive impairment in the emergency department.Journal of the American Geriatrics Society · 2022Trial
- The role and utility of artificial intelligence and machine learning for diagnostic prediction in general practice.The European journal of general practice · 2026Article
- Contribution and legacy: a qualitative study of older people's attitudes about sharing their routinely collected health data for research purposes in New Zealand.BMC medical ethics · 2025Article
- Five-year dementia prediction and decision support system based on real-world data.Frontiers in aging neuroscience · 2025Article
- Predicting the onset of Alzheimer's disease and related dementia using electronic health records: findings from the cache county study on memory in aging (1995-2008).BMC medical informatics and decision making · 2024Article
- Predicting the onset of Alzheimer's disease and related dementia using Electronic Health Records: Findings from the Cache County Study on Memory in Aging (1995-2008).Research square · 2024Article
- Psychometric Evaluation of a Tablet-Based Tool to Detect Mild Cognitive Impairment in Older Adults: Mixed Methods Study.Journal of medical Internet research · 2024Article
- Uncovering the burden of hidradenitis suppurativa misdiagnosis and underdiagnosis: a machine learning approach.Frontiers in medical technology · 2024Article
- Artificial intelligence approaches for early detection of neurocognitive disorders among older adults.Frontiers in computational neuroscience · 2024Article
- Artificial intelligence for dementia-Applied models and digital health.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- Identifying Probable Dementia in Undiagnosed Black and White Americans Using Machine Learning in Veterans Health Administration Electronic Health Records.Big data and cognitive computing · 2023Article
- Immediate word recall in cognitive assessment can predict dementia using machine learning techniques.Alzheimer's research & therapy · 2023Article
- Article
- Machine learning in general practice: scoping review of administrative task support and automation.BMC primary care · 2023Article
- Digital detection of dementia (DTrials · 2022Article
- Identifying individuals with undiagnosed post-traumatic stress disorder in a large United States civilian population - a machine learning approach.BMC psychiatry · 2022Article
- Screening dementia and predicting high dementia risk groups using machine learning.World journal of psychiatry · 2022Article
- Using Bayesian networks to identify musculoskeletal symptoms influencing the risk of developing psoriatic arthritis in people with psoriasis.Rheumatology (Oxford, England) · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUp 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.
aimThe 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 &
settingThe 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.
methodRoutinely 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.
resultsThe model obtained sensitivity and specificity values of 84.47% and 86.67%, respectively.
conclusionThe 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.
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