An Adaptive Machine Learning Triage Framework for Predicting Alzheimer’s Disease Progression

Richard Hou, Shengpu Tang, Wei Jin

Keywords: disease progression prediction, Alzheimer's disease, uncertainty estimation

TL;DR: We proposed a novel two-stage ML triage system that selectively determines the needs of advanced testing for better AD prediction, ultimately reducing cost by 20% while achieving high AUROC.

Track: Findings

Abstract: Accurate predictions of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) can enable effective personalized therapy. While cognitive tests and clinical data are routinely collected, they lack the predictive power of PET scans and CSF biomarker analysis, which are prohibitively expensive to obtain for every patient. To address this cost-accuracy dilemma, we design a two-stage machine learning framework that selectively obtains advanced, costly features based on their predicted "value of information". We apply our framework to predict AD progression for MCI patients using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our framework reduces the need for advanced testing by 20\% while achieving a test AUROC of 0.929, comparable to the model that uses both basic and advanced features (AUROC=0.915, $p$=0.1010). We also provide an example interpretability analysis showing how one may explain the triage decision. Our work presents an interpretable, data-driven framework that optimizes AD diagnostic pathways and balances accuracy with cost, representing a step towards making early, reliable AD prediction more accessible in real-world practice. Future work should consider multiple categories of advanced features and larger-scale validation.

General Area: Applications and Practice

Specific Subject Areas: Supervised Learning, Other (Use Sparingly)

Data And Code Availability: Yes

Ethics Board Approval: No

Entered Conflicts: I confirm the above

Anonymity: I confirm the above

Code URL: https://github.com/chardhou-cpu/Triage-Framework-AD

Submission Number: 166