Abstract Details

Title Machine learning methods to predict amyloid positivity using domain scores from cognitive tests

Topic Aging, Dementia, and Behavioral Neurology

Presentation(s) P4 - Poster Session 4 (8:00 AM-9:00 AM)

Poster/Presentation
Number 7-003

Objective Accurately predicting Amyloid-β (Aβ) status of participants by using machine learning (ML) models based on easily accessible data, could improve the effectiveness of AD clinical trials. We will develop optimal ML models for each subpopulation stratified by sex and disease stages using sub scores from screening neurological tests.

Background Amyloid-β (Aβ) is the target in many clinical trials for Alzheimer’s disease (AD). Preclinical AD patients are heterogeneous with regards to different backgrounds and diagnosis.

Design/Methods We will develop optimal ML models for each subpopulation stratified by sex and disease stages using sub scores from screening neurological tests. Data from the AD Neuroimaging Initiative (ADNI) were used to build the ML models, for three groups: individuals with significant memory concern, early mild cognitive impairment (MCI), and late MCI. Data were further separated into 6 groups by disease stage (3 levels) and sex (2 categories). The outcome was defined as the Aβ status confirmed by the PET imaging, and the features include demographic data, newly identified risk factors, screening tests, and the domain scores from screening tests. Monte Carlo simulation studies were used together with k-fold cross-validation technique to compute model performance metric. We also develop a new feature selection method based on the stochastic ordering to avoiding searching all possible combinations of features.

Results Accuracy of the identified optimal model for SMC male was over 90% by using domain scores, and accuracy for LMCI female was above 86%. Domain scores can improve the ML model prediction as compared to the total scores.

Conclusions Accurate ML prediction models can identify the proper population for AD clinical trials.

Authors/Disclosures
Guogen Shan, PhD PRESENTER	Dr. Shan has nothing to disclose.
Jiong Shi, MD, FAAN (Lou Ruvo Center for Brain Health- Cleveland Clinic Nevada)	Dr. Shi has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Roche.
Charles Bernick, MD	Dr. Bernick has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Lilly. Dr. Bernick has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Optina. Dr. Bernick has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Eisai. Dr. Bernick has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Corium. Dr. Bernick has stock in Aurora. The institution of Dr. Bernick has received research support from UFC. The institution of Dr. Bernick has received research support from Top Rank Promotions. The institution of Dr. Bernick has received research support from Haymon Boxing.

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