TY - JOUR AU - Ford, Elizabeth AU - Rooney, Philip AU - Oliver, Seb AU - Hoile, Richard AU - Hurley, Peter AU - Banerjee, Sube AU - van Marwijk, Harm AU - Cassell, Jackie PY - 2019 DA - 2019/12/02 TI - Identifying undetected dementia in UK primary care patients: a retrospective case-control study comparing machine-learning and standard epidemiological approaches JO - BMC Medical Informatics and Decision Making SP - 248 VL - 19 IS - 1 AB - Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a formal diagnosis in United Kingdom health systems and many receive it late in the disease process, there is ample room for improvement. The policy of the UK government and National Health Service (NHS) is to increase rates of timely dementia diagnosis. We used data from general practice (GP) patient records to create a machine-learning model to identify patients who have or who are developing dementia, but are currently undetected as having the condition by the GP. SN - 1472-6947 UR - https://doi.org/10.1186/s12911-019-0991-9 DO - 10.1186/s12911-019-0991-9 ID - Ford2019 ER -