CognitiveTwin: Robust Multi-Modal Digital Twins for Predicting Cognitive Decline in Alzheimer's Disease

TL;DR

CognitiveTwin is a multi-modal digital twin framework that predicts individual cognitive decline in Alzheimer's disease, demonstrating accuracy, fairness, and robustness to missing data using Transformer and Deep Markov Models.

Contribution

It introduces a novel multi-modal, Transformer-based digital twin model that captures temporal dynamics for personalized Alzheimer's disease progression prediction.

Findings

Accurately predicts cognitive decline trajectories.

Demonstrates fairness across different demographics.

Resilient to missing-not-at-random data patterns.

Abstract

Predicting individual cognitive decline in Alzheimer's disease (AD) is difficult due to the heterogeneity of disease progression. Reliable clinical tools require not only high accuracy but also fairness across demographics and robustness to missing data. We present CognitiveTwin, a digital twin framework that predicts patient-specific cognitive trajectories. The model integrates multi-modal longitudinal data (cognitive scores, magnetic resonance imaging, positron emission tomography, cerebrospinal fluid biomarkers, and genetics). We use a Transformer-based architecture to fuse these modalities and a Deep Markov Model to capture temporal dynamics. We trained and evaluated the framework using data from 1,666 patients in the TADPOLE (Alzheimer's Disease Neuroimaging Initiative) dataset. We assessed the model for prediction error, demographic fairness, and robustness to missing-not-at-random (MNAR) data patterns. ognitiveTwin provides accurate and personalized predictions of cognitive decline. Its demonstrated fairness across patient demographics and resilience to clinical dropout make it a reliable tool for clinical trial enrichment and personalized care planning.