UCLA researchers developed an AI model using EHR data to better detect undiagnosed Alzheimer's disease, especially in underrepresented groups.
Key Details
- 1The AI leverages semi-supervised positive unlabeled learning to identify probable Alzheimer's cases from electronic health records (EHRs).
- 2Tested on data from over 97,000 UCLA Health patients across multiple ethnic groups.
- 3Achieved 77–81% sensitivity in various populations compared to 39–53% for traditional models.
- 4Model validation included polygenic risk scores (APOE ε4 allele), supporting accuracy of predictions.
- 5Tool addresses underdiagnosis, which especially affects African American and Hispanic/Latino communities.
- 6Planned prospective validation in other health systems; patent application filed for framework.
Why It Matters
AI-driven detection of undiagnosed Alzheimer's may support earlier intervention, which is critical as new treatments emerge. Importantly, this model addresses equity concerns by reducing diagnostic bias across diverse populations, a key consideration for both clinical and AI application in healthcare.
Source
EurekAlert
Related News
•EurekAlert
NIH-Backed AI Model Predicts Cancer Survival Using Single-Cell Data
Researchers have developed scSurvival, a machine learning tool that uses single-cell tumor data to accurately predict cancer patient survival and identify high-risk cell populations.
•EurekAlert
Deep Learning Pathomics Platform Improves Immunotherapy Prediction in Lung Cancer
A deep learning pathomics platform accurately predicts immunotherapy response in metastatic NSCLC using routine pathology slides.
•EurekAlert
AI Pathology Model Outperforms PD-L1 in Predicting NSCLC Immunotherapy Response
MD Anderson's Path-IO machine learning platform accurately predicts immunotherapy responses in metastatic non-small cell lung cancer, surpassing current biomarker standards.