INDIANAPOLIS – A new study by researchers from Regenstrief Institute, Indiana University and Purdue University presents their low cost, scalable methodology for the early identification of individuals at risk of developing dementia. While the condition remains incurable, there are a number of common risk factors that, if targeted and addressed, can potentially reduce the odds of developing dementia or slow the pace of cognitive decline.
"Detection of dementia risk is important for appropriate care management and planning," said study senior author Malaz Boustani, M.D., MPH., of Regenstrief Institute and IU School of Medicine. "We wanted to solve the problem of identifying individuals early on who are likely to develop dementia with a solution that is both scalable and cost effective for the healthcare system.
"To do this, we use existing information – passive data – already in the patient's medical notes for what we call zero-minute assessment at less than a dollar cost. Decision-focused content selection methodology is used to develop an individualized dementia risk prediction or to demonstrate evidence of mild cognitive impairment."
This technique utilizes machine learning to select a subset of phrases or sentences from the medical notes in a patient's electronic health record (EHR) written by a doctor, a nurse, a social worker or other provider that are relevant to the target outcome over a defined observation period. Medical notes are narratives in an EHR that describe the health of the patient in free text format.
