OPTIMUS: Predicting Multivariate Outcomes in Alzheimer's Disease Using Multi-Modal Data Amidst Missing Values

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Christelle Schneuwly Diaz, Duy-Thanh Vu, Julien S. Bodelet, Duy-Cat Can, Guillaume Blanc, Haiteng Jiang, Lin Yao, Guiseppe Pantaleo, Oliver Y. Chén

Published: 2026, Last Modified: 05 May 2026IEEE Trans. Biomed. Eng. 2026EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Objective: Alzheimer's disease, a progressive neurodegenerative disorder, involves neural, genetic, and proteomic factors and impacts multiple cognitive and behavioral domains. Traditional AD prediction largely focuses on univariate disease outcomes, such as disease stages and severity. Multimodal data provide richer disease information than a single modality and may enhance prediction, but are often incomplete due to missing measurements. Recent machine learning approaches show promise in improving prediction accuracy, but their biological relevance remains insufficiently understood. Methods: Integrating missing data analysis, predictive modeling, multimodal data analysis, and explainable AI (XAI), we propose OPTIMUS, a predictive and explainable machine-learning framework, to unveil the many-to-many predictive pathways between multimodal input data and multivariate disease outcomes amidst missing values. Results: OPTIMUS first applies imputation to handle missing data within each modality type while optimizing overall prediction accuracy. It then maps multimodal biomarkers to multivariate outcomes using machine learning and extracts biomarkers that are respectively predictive of each outcome. Finally, outcome prediction targetedly integrating multimodality with unavailable signals (OPTIMUS) incorporates XAI to explain the identified multimodal biomarkers. Conclusion: Using data from 348 cognitively normal subjects, 601 persons with mild cognitive impairment, and 256 AD patients, OPTIMUS identifies neural and transcriptomic signatures that jointly but differentially predict outcomes related to memory, executive function, visuospatial function, and language. Significance: Our work demonstrates the potential of building a predictive and biologically explainable machine-learning framework to uncover multimodal biomarkers that capture disease profiles across varying cognitive landscapes. The results improve our understanding of the complex many-to-many pathways in AD.