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

arXiv: https://arxiv.org/abs/2404.03208 · HTML: https://mlsanity.com/paper/2404.03208/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 70 references that are in the map.
- First posted on arXiv 4 Apr 2024.

## Builds on (most cited first, 7 of 7)

- [BRITS: Bidirectional Recurrent Imputation for Time Series](https://mlsanity.com/paper/1805.10572/) (2018)
- [Multimodal Attention-based Deep Learning for Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2206.08826/) (2022)
- [Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review](https://mlsanity.com/paper/2108.04174/) (2021)
- [Multiple Kernel Learning in the Primal for Multi-modal Alzheimer's Disease Classification](https://mlsanity.com/paper/1310.0890/) (2013)
- [Normative Modeling using Multimodal Variational Autoencoders to Identify Abnormal Brain Structural Patterns in Alzheimer Disease](https://mlsanity.com/paper/2110.04903/) (2021)
- [Predicting Time-to-conversion for Dementia of Alzheimer's Type using Multi-modal Deep Survival Analysis](https://mlsanity.com/paper/2205.01188/) (2022)
- [Improving Normative Modeling for Multi-modal Neuroimaging Data using mixture-of-product-of-experts variational autoencoders](https://mlsanity.com/paper/2312.00992/) (2023)

## Built on by (most cited first, 1 of 1)

- [Transition-Based Digital Twin Modelling for Alzheimer's Disease under Sparse Longitudinal Data](https://mlsanity.com/paper/2606.09671/) (2026)

## Abstract (arXiv)

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.