UMass Lowell researchers have found an exciting way to detect Alzheimer’s disease much earlier than typical diagnoses. Using artificial intelligence to sift through clinical notes in electronic health records, a team led by Professor Hong Yu can identify the risk up to 15 years before a formal diagnosis.
Currently, around 7.2 million Americans aged 65 and older have Alzheimer’s dementia, and this number is projected to rise to 13.8 million by 2060, according to the Alzheimer’s Association. An early diagnosis can change the game. It opens the door for early behavioral therapies and medications that can slow the disease’s progression. An early diagnosis might also save up to $7 trillion in healthcare costs over time, according to studies carried out by the Alzheimer’s Association.
Traditional methods of diagnosing Alzheimer’s often involve invasive procedures, like spinal taps, or costly imaging studies. “Existing diagnostic techniques can be both complicated and expensive,” Yu explains.
The recent study received $6 million in funding from the National Institutes of Health and was published in January in Communications Medicine. The research relied on records from 61,537 patients diagnosed with Alzheimer’s and over 234,000 without the disease, facilitated by the U.S. Department of Veterans Affairs.
In analyzing the records, researchers identified key phrases—like “mood,” “pain,” and “wandering”—that stood out in those diagnosed with Alzheimer’s compared to others. These early indicators allowed the team to spot risks far ahead of time.
“This data set is among the most comprehensive in the U.S., covering patients from all 50 states,” Yu mentions. The Veterans Health Administration’s long-term patient care produces valuable data for research, sometimes spanning 20 years.
Yu’s team isn’t just focused on Alzheimer’s; they also investigate other social issues using AI. “We aim to harness technology to gain insights into human health and drive healthier behaviors,” she says. Understanding factors like education, smoking habits, and social isolation can enhance the predictive accuracy of their models.
This research highlights the importance of early intervention and a more nuanced view of health risk. By integrating social determinants into current models, Yu hopes to develop solutions that lead to better health outcomes. “We want to help people,” she adds, expressing optimism about future advancements in understanding Alzheimer’s and related health issues.
As this research evolves, new studies may reveal more about the complex interactions between social factors and health, giving hope to millions affected by Alzheimer’s and their families. For further insights, you can explore the latest findings from the Alzheimer’s Association.
Source link
alzheimer’s disease,artificial intelligence,health,higher education,lowell,public education,umass lowell,veterans,veterans administration,news,connecticut,massachusetts,middlesex county,education,local news
