A new machine-learning model that scans routinely collected NHS data has shown promising signs of being able to predict undiagnosed dementia in primary care. The results from the feasibility study suggest that the model could significantly reduce the number of those living with undiagnosed dementia | BJGP Open | via ScienceDaily
Improving dementia care through increased and timely diagnosis is a priority for the NHS, yet around half of those living with dementia are unaware they live with the condition. Now a new machine-learning model that scans routinely collected NHS data has shown promising signs of being able to predict undiagnosed dementia in primary care.
Led by the University of Plymouth, the study collected Read-encoded data from 18 consenting GP surgeries across Devon, UK, for 26,483 patients aged over 65. The Read codes — a thesaurus of clinical terms used to summarise clinical and administrative data for UK GPs — were assessed on whether they may contribute to dementia risk, with factors included such as weight and blood pressure. These codes were used to train a machine-learning classification model to identify patients that may have underlying dementia.
The results showed that 84% of people who had dementia were detected as having the condition (sensitivity value) while 87% of people without dementia had been correctly acknowledged as not having the condition (specificity value), according to the data.
These results indicate that the model can detect those with underlying dementia with an accuracy of 84%. This suggests that the machine-learning model could, in future, significantly reduce the number of those living with undiagnosed dementia — from around 50% (current estimated figure) to 8%.
Full story at ScienceDaily
Full reference: Jammeh, E. A. et al. | Machine-learning based identification of undiagnosed dementia in primary care: a feasibility study | BJGP Open | 2018