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

arXiv cs.AI / 4/27/2026

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Key Points

CognitiveTwinは、アルツハイマー病（AD）における個人ごとの認知機能の低下（cognitive decline）を予測するマルチモーダル・デジタルツインの枠組みです。
認知スコア、MRI、PET、髄液バイオマーカー、遺伝情報といった縦断的な複数データをTransformerで統合し、時間的ダイナミクスをDeep Markov Modelで捉えます。
TADPOLEデータセットの1666人の患者データで学習・評価され、予測誤差に加えて、人口統計的な公平性とMNAR（欠測が無作為でない）パターンへの頑健性を検証しています。
公平性と臨床での脱落（ドロップアウト）への耐性が示されたことで、臨床試験の被験者選定（trial enrichment）や個別ケア計画に資する可能性が示されています。

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.