Uncertainty-Aware Multi-Modal Ensembling for Severity Prediction of Alzheimer's Dementia

Abstract

Reliability in Neural Networks (NNs) is crucial in safety-critical applications like healthcare, and uncertainty estimation is a widely researched method to highlight the confidence of NNs in deployment. In this work, we propose an uncertainty-aware boosting technique for multi-modal ensembling to predict Alzheimer's Dementia Severity. The propagation of uncertainty across acoustic, cognitive, and linguistic features produces an ensemble system robust to heteroscedasticity in the data. Weighing the different modalities based on the uncertainty estimates, we experiment on the benchmark ADReSS dataset, a subject-independent and balanced dataset, to show that our method outperforms the state-of-the-art methods while also reducing the overall entropy of the system. This work aims to encourage fair and aware models. The source code is available at https://github.com/wazeerzulfikar/alzheimers-dementia

To appear at NeurIPS Machine Learning for Health (ML4H) 2020

References in corpus (3)

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• Multi-modal fusion with gating using audio, lexical and disfluency features for Alzheimer's Dementia recognition from spontaneous speech
• Why have a Unified Predictive Uncertainty? Disentangling it using Deep Split Ensembles