A machine learning-based model using data routinely gathered in primary care identified patients with dementia in such settings, according to research recently published in BJGP Open.
“Improving dementia care through increased and timely diagnosis is a priority, yet almost half of those living with dementia do not receive a timely diagnosis,” Emmanuel A. Jammeh, PhD, of the science and engineering department at Plymouth University in the United Kingdom, and colleagues wrote.
“A cost-effective tool that can be used by [primary care providers] to identify patients likely to be living with dementia, based only on routine data would be extremely useful. Such a tool could be used to select high-risk patients who could be invited for targeted screening,” they added.
The researchers used Read codes, a set of clinical terms used in the U.K. to summarize data for general practice, to develop a machine learning-based model to identify patients with dementia. The Read codes were selected based on their significant association with patients with dementia, and included codes for risk factors, symptoms and behaviors that are collected in primary care. To test the model, researchers collected Read-encoded data from 26,483 patients living in England aged 65 years and older.
Jammeh and colleagues found that their machine-based model achieved a sensitivity of 84.47% and a specificity of 86.67% for identifying dementia.
“This is the first demonstration of a machine-learning approach to identifying dementia using routinely collected [National Health Service] data, researchers wrote.
“With the expected growth in dementia prevalence, the number of specialist memory clinics may be insufficient to meet the expected demand for diagnosis. Furthermore, although current ‘gold standards’ in dementia diagnosis may be effective, they involve the use of expensive neuroimaging (for example, positron emission tomography scans) and time-consuming neuropsychological assessments which is not ideal for routine screening of dementia,” they continued.
The model will be evaluated with other datasets, and have its validation tested “more extensively” at general practitioner practices in the future, Jammeh and colleagues added. – by Janel Miller