HiMAL: A Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting and explaining Alzheimer disease progression

TL;DR

The paper introduces HiMAL, a multimodal hierarchical multi-task framework that predicts and explains Alzheimer’s disease progression using longitudinal data, outperforming existing models and providing interpretable risk assessments for clinical use.

Contribution

HiMAL is a novel multimodal, hierarchical, multi-task learning framework that improves prediction accuracy and offers interpretability for Alzheimer’s disease progression from routine clinical data.

Findings

HiMAL achieved AUROC of 0.923, outperforming baselines.

Imaging and cognitive scores contributed most to predictions.

Model explanations can predict cognitive decline 6 months in advance.

Abstract

Objective: We aimed to develop and validate a novel multimodal framework HiMAL (Hierarchical, Multi-task Auxiliary Learning) framework, for predicting cognitive composite functions as auxiliary tasks that estimate the longitudinal risk of transition from Mild Cognitive Impairment (MCI) to Alzheimer Disease (AD). Methods: HiMAL utilized multimodal longitudinal visit data including imaging features, cognitive assessment scores, and clinical variables from MCI patients in the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset, to predict at each visit if an MCI patient will progress to AD within the next 6 months. Performance of HiMAL was compared with state-of-the-art single-task and multi-task baselines using area under the receiver operator curve (AUROC) and precision recall curve (AUPRC) metrics. An ablation study was performed to assess the impact of each input modality on model performance. Additionally, longitudinal explanations regarding risk of disease progression were provided to interpret the predicted cognitive decline. Results: Out of 634 MCI patients (mean [IQR] age : 72.8 [67-78], 60% men), 209 (32%) progressed to AD. HiMAL showed better prediction performance compared to all single-modality singe-task baselines (AUROC = 0.923 [0.915-0.937]; AUPRC= 0.623 [0.605-0.644]; all p<0.05). Ablation analysis highlighted that imaging and cognition scores with maximum contribution towards prediction of disease progression. Discussion: Clinically informative model explanations anticipate cognitive decline 6 months in advance, aiding clinicians in future disease progression assessment. HiMAL relies on routinely collected EHR variables for proximal (6 months) prediction of AD onset, indicating its translational potential for point-of-care monitoring and managing of high-risk patients.