Scientists Develop Noninvasive Method to Predict Onset of Dementia
Information gathered from routine visits to the doctor is enough to accurately predict a person's risk of developing Alzheimer's disease and related dementias, according to new research led by scientists from Regenstrief Institute, Indiana University, and Merck. The researchers developed and tested machine learning algorithms using data from electronic medical records to identify patients who may be at risk for developing the dementia.
At least 50% of older primary care patients living with Alzheimer's disease and related dementias never receive a diagnosis. And many more live with symptoms for two to five years before being diagnosed. Currently, tests to screen for dementia risk are invasive, time-consuming, and expensive.
"The great thing about this method is that it's passive, and it provides similar accuracy to the more intrusive tests that are currently used," says lead researcher Malaz Boustani, MD, MPH, a research scientist at Regenstrief Institute and a professor at Indiana University School of Medicine. "This is a low cost, scalable solution that can provide substantial benefit to patients and their families by helping them prepare for the possibility of life with dementia and enabling them to take action."
Developing Machine Learning Algorithms for Predicting Dementia
In order to train the algorithms, researchers gathered data on patients from the Indiana Network for Patient Care. The models used information on prescriptions and diagnoses, which are structured fields, as well as medical notes, which are free text, to predict the onset of dementia. Researchers found that the free text notes were the most valuable to helping identify people at risk of developing the disease.
"This research is exciting because it potentially provides significant benefit to patients and their families," says Patrick Monahan, PhD, a study author from Indiana University School of Medicine and a Regenstrief affiliate scientist. "Clinicians can provide education on behavior and habits to help patients cope with their symptoms and live a better quality of life."
Zina Ben Miled, PhD, MS, a study author from the Purdue School of Engineering and Technology at Indiana University–Purdue University Indianapolis and a Regenstrief affiliate scientist, says, "The early risk identification allows an opportunity for doctors and families to put a care plan in place. I know from experience what a burden it can be to deal with a dementia diagnosis. The window provided by this test is so important to help improve the quality of life for both patients and their families."
In addition to the benefit to families, these methods can also provide significant cost savings for patients and health systems. They replace the need for expensive tests and allow clinicians to screen entire populations to identify those most at risk. Delaying the onset of symptoms also saves a significant amount of money on treatment.
The next step is to deploy these machine learning algorithms in real-life clinics to test if they help identify more true cases of dementia as well as to learn how they impact a patient's willingness to follow up on the results.
Source: Regenstrief Institute