ACUMEN: Decision-Aware Multimodal Distillation for Adaptive Cognitive Assessment

Diagnosing cognitive impairment requires choosing which cognitive tests to administer. A comprehensive neuropsychological evaluation can take several hours, yet omitting an informative test can leave impairment undetected or its cause unresolved. Each choice is also made from an incomplete record: only some of the current visit's tests have results, and earlier visits may be absent or may have used a different version of the test battery. Positron emission tomography (PET) and cerebrospinal fluid (CSF) biomarkers are informative but costly or invasive, and they are usually ordered only after cognitive testing. Research cohorts record these measures and magnetic resonance imaging (MRI) for a subset of participants, often at different times from the clinical visit. These modalities can therefore supervise training, but cannot be assumed when a test is chosen. ACUMEN uses them to train a learner that diagnoses and selects tests from clinical information. It represents revealed scores, prior measurements, timing, and absent modalities, and distills multimodal current-state and temporal representations into separate clinical pathways for diagnosis and acquisition. On six-category final-diagnosis classification with National Alzheimer's Coordinating Center (NACC) data, the model reaches 93.2% accuracy with full multimodal input and 90.1% with clinical inputs alone, compared with 86.2% for a clinical multilayer perceptron. Training with modality dropout reduces the full-input to clinical-only accuracy gap from 8.6 to 3.1 percentage points. To extend this transfer to test selection, we specify a privileged teacher that predicts how much each test would reduce the loss of a fixed diagnostic predictor. A clinical-only acquisition student learns from the observed reductions and from teacher predictions fitted on other participants, keeping the observed targets when richer data are absent. Our analysis separates errors that change the ranking of tests from a common offset that affects only the decision to stop. The classification results establish the predictive foundation. The effect of this supervision on test selection has not yet been measured, and we specify the study that would measure it.