Revolutionary Low-Cost Method Identifies Dementia Risk Early for Under $1

Risk detection plays a crucial role in care management and can potentially influence the progression of the disease.

Researchers from the Regenstrief Institute, Indiana University, and Purdue University have introduced a cost-effective, scalable approach for the early detection of individuals at risk for dementia. Although dementia remains incurable, addressing common risk factors may help lower the likelihood of developing the condition or slow the progression 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.

Extracting Insights from Medical Notes

Information selected for extraction from the medical notes to predict dementia risk might include clinician comments, patient remarks, blood pressure or cholesterol values over time, observations of mental status by a family member, or a medication history — including prescription and over-the-counter drugs as well as “natural” remedies and supplements.

Predicting dementia risk helps the patient, the family, and healthcare providers access resources such as support groups and the Centers for Medicare and Medicaid GUIDE model program, which supports keeping individuals in their homes longer.

It could also encourage clinician deprescribing of medications commonly taken by older adults but known to negatively affect the brain as well as conversations with the patient about over-the-counter drugs with similar characteristics. Knowing dementia risk might prompt physician consideration of newly FDA-approved amyloid-lowering therapies which alter the trajectory of Alzheimer’s disease.

Machine Learning and Clinical Validation

“Our methodology combines both supervised and unsupervised machine learning in order to extract sentences which are relevant to dementia from the large amount of medical notes readily available for each patient,” said study co-author Zina Ben Miled, PhD, M.S., a Regenstrief Institute affiliate scientist and a former Purdue University in Indianapolis faculty member. “In addition to improving predictive accuracy, this allows the health provider to quickly confirm cognitive impairment by reviewing the specific text used to drive the risk assessment by our language model.”

“Regenstrief Institute and Indiana University investigators have been pioneers in demonstrating the utility of electronic health records since the early 1970s. Given the enormous amount of effort by both clinicians and patients that goes into capturing EHR data, the goal must be to seek maximal clinical value from these data even beyond their central role in medical care,” said study co-author Paul Dexter, M.D., of Regenstrief and IU School of Medicine. “By applying machine learning methods to identify patients at high risk of dementia in the future, this study provides an excellent and innovative example of the clinical value that is achievable from EHRs. The early identification of dementia will prove increasingly vital particularly as new treatments are developed.”

While the ultimate beneficiaries of the use of the new technique are patients and caregivers, providing zero-minute assessment at less than a dollar cost has a clear upside for primary care clinicians who are overburdened and often lack the time and training needed to administer specialized cognitive tests.

The study authors’ 5-year clinical trial of their risk prediction tool, being conducted in Indianapolis and Miami, is in its final year. Lessons learned from this trial will enable them to advance the utility of the dementia risk prediction framework in primary care practices. The researchers plan future work on the fusion of medical notes with other information contained in electronic health records as well as environmental data.

Reference: “Dementia risk prediction using decision-focused content selection from medical notes” by Shengyang Li, Paul Dexter, Zina Ben-Miled and Malaz Boustani, 18 September 2024, Computers in Biology and Medicine.
DOI: 10.1016/j.compbiomed.2024.109144

This research is supported by the National Institutes of Health’s National Institute on Aging grant R01AG069765 (PIs: Malaz Boustani, M.D., MPH; Zina Ben Miled, PhD, and James Galvin, M.D., MPH).

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