{"id":32956,"date":"2025-12-12T13:00:52","date_gmt":"2025-12-12T07:15:52","guid":{"rendered":"https:\/\/www.revoscience.com\/en\/?p=32956"},"modified":"2025-12-12T13:00:55","modified_gmt":"2025-12-12T07:15:55","slug":"researchers-develop-ai-tool-to-identify-undiagnosed-alzheimers-cases-while-reducing-disparities","status":"publish","type":"post","link":"https:\/\/www.revoscience.com\/en\/researchers-develop-ai-tool-to-identify-undiagnosed-alzheimers-cases-while-reducing-disparities\/","title":{"rendered":"Researchers develop AI Tool to identify undiagnosed Alzheimer’s cases while reducing disparities"},"content":{"rendered":"\n

Machine learning model detects missed diagnoses with high accuracy across populations, addressing critical healthcare inequities<\/em><\/strong><\/p>\n\n\n\n

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ai-generated image<\/sup><\/em><\/figcaption><\/figure>\n\n\n\n

Researchers at UCLA have developed an artificial intelligence tool that can use electronic health records to identify patients with undiagnosed Alzheimer\u2019s disease, addressing a critical gap in Alzheimer\u2019s care: significant underdiagnosis, particularly among underrepresented communities.<\/p>\n\n\n\n

Disparities in Alzheimer\u2019s and dementia diagnosis among certain populations have been a longstanding issue. African Americans are nearly twice as likely to have the neurodegenerative disease compared to non-Hispanic whites but only 1.34 times as  likely to receive a diagnosis. Similarly, Hispanic and Latino people are 1.5 times more likely to have the disease but only 1.18 times as likely to be diagnosed.<\/p>\n\n\n\n

\u201cAlzheimer’s disease is the sixth leading cause of death in the United States and affects 1 in 9 Americans aged 65 and older,” said Dr. Timothy Chang<\/a>, the study’s corresponding author from UCLA Health Department of Neurology. \u201cThe gap between who actually has the disease and who gets diagnosed is substantial, and it’s more significant in underrepresented communities.\u201d<\/p>\n\n\n\n

Previous research has leveraged machine learning models to attempt to predict Alzheimer\u2019s disease using electronic health records, but were designed using traditional frameworks that may not account for certain diagnostic biases.<\/p>\n\n\n\n

The new model developed by the UCLA team took a different approach, known as semi-supervised positive unlabeled learning, that was specifically designed to promote fairness while maintaining high accuracy.<\/p>\n\n\n\n

The electronic health records from more than 97,000 patients at UCLA Health, including those with confirmed diagnoses of Alzheimer\u2019s disease and unconfirmed cases.<\/p>\n\n\n\n

The model achieved sensitivity rates of 77 to 81% across non-Hispanic white, non-Hispanic African American, Hispanic\/Latino, and East Asian groups compared to the 39 to 53% sensitivity of conventional supervised models.<\/p>\n\n\n\n

UCLA researchers built on previous AI models used to predict various diseases including Alzheimer\u2019s disease but had gaps as far as reducing biases and disparities. The UCLA tools analyzed patterns in the health records such as diagnosis, age, and other clinical factors. Key predictive features for Alzheimer\u2019s were also identified, including both neurological indicators such as memory loss and unexpected patterns such as decubitus ulcers and heart palpitations that may signal undiagnosed cases.<\/p>\n\n\n\n

Unlike traditional approaches that require confirmed diagnoses for all training data, the UCLA model learns from both confirmed cases and patients with unknown Alzheimer’s status. The researchers incorporated fairness measures throughout the model’s development, using population-specific criteria to reduce diagnostic disparities.<\/p>\n\n\n\n

The tool was validated using multiple approaches, including genetic data. Patients predicted to have undiagnosed Alzheimer’s showed significantly higher polygenic risk scores and genetic markers for the disease, known as APOE \u03b54 allele counts, compared to those predicted not to have it. Chang said the tool could help clinicians identify high-risk patients who may benefit from further evaluation or screening. Early identification is crucial as new Alzheimer’s treatments become available and lifestyle interventions can slow disease progression.<\/p>\n\n\n\n

The research team plans to validate the model prospectively in partnering health systems to assess its generalizability and clinical utility before potential implementation in routine care.<\/p>\n\n\n\n

\u201cBy ensuring equitable predictions across populations, our model can help remedy significant underdiagnosis in underrepresented populations,\u201d Chang said. \u201cIt has the potential to address disparities in Alzheimer’s diagnosis.\u201d<\/p>\n\n\n\n

The\u00a0study<\/a>\u00a0appears in the journal\u00a0npj Digital Medicine<\/em>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Researchers at UCLA have developed an artificial intelligence tool that can use electronic health records to identify patients with undiagnosed Alzheimer\u2019s disease, addressing a critical gap in Alzheimer\u2019s care: significant underdiagnosis, particularly among underrepresented communities.<\/p>\n","protected":false},"author":2,"featured_media":32957,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[163,6,17],"tags":[],"class_list":["post-32956","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-health","category-research"],"featured_image_urls":{"full":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/12\/undiagnosed-Alzheimers-cases.webp",768,576,false],"thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/12\/undiagnosed-Alzheimers-cases-200x200.webp",200,200,true],"medium":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/12\/undiagnosed-Alzheimers-cases-675x506.webp",675,506,true],"medium_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/12\/undiagnosed-Alzheimers-cases.webp",750,563,false],"large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/12\/undiagnosed-Alzheimers-cases.webp",750,563,false],"1536x1536":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/12\/undiagnosed-Alzheimers-cases.webp",768,576,false],"2048x2048":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/12\/undiagnosed-Alzheimers-cases.webp",768,576,false],"ultp_layout_landscape_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/12\/undiagnosed-Alzheimers-cases.webp",768,576,false],"ultp_layout_landscape":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/12\/undiagnosed-Alzheimers-cases-768x570.webp",768,570,true],"ultp_layout_portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/12\/undiagnosed-Alzheimers-cases-600x576.webp",600,576,true],"ultp_layout_square":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/12\/undiagnosed-Alzheimers-cases-600x576.webp",600,576,true],"newspaper-x-single-post":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/12\/undiagnosed-Alzheimers-cases-760x490.webp",760,490,true],"newspaper-x-recent-post-big":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/12\/undiagnosed-Alzheimers-cases-550x360.webp",550,360,true],"newspaper-x-recent-post-list-image":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/12\/undiagnosed-Alzheimers-cases-95x65.webp",95,65,true],"web-stories-poster-portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/12\/undiagnosed-Alzheimers-cases-640x576.webp",640,576,true],"web-stories-publisher-logo":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/12\/undiagnosed-Alzheimers-cases-96x96.webp",96,96,true],"web-stories-thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/12\/undiagnosed-Alzheimers-cases-150x113.webp",150,113,true]},"author_info":{"info":["RevoScience"]},"category_info":"AI<\/a> Health<\/a> Research<\/a>","tag_info":"Research","comment_count":"0","_links":{"self":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/32956","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/comments?post=32956"}],"version-history":[{"count":1,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/32956\/revisions"}],"predecessor-version":[{"id":32958,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/32956\/revisions\/32958"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media\/32957"}],"wp:attachment":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media?parent=32956"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/categories?post=32956"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/tags?post=32956"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}