By 2050, it is estimated that the number of worldwide Alzheimer�s disease (AD) patients will quadruple from the\ncurrent number of 36 million, while no proven disease-modifying treatments are available. At present, the underlying\ndisease mechanisms remain under investigation, and recent studies suggest that the disease involves multiple\netiological pathways. To better understand the disease and develop treatment strategies, a number of ongoing studies\nincluding the Alzheimer�s Disease Neuroimaging Initiative (ADNI) enroll many study participants and acquire a large\nnumber of biomarkers from various modalities including demographic, genotyping, fluid biomarkers, neuroimaging,\nneuropsychometric test, and clinical assessments. However, a systematic approach that can integrate all the collected\ndata is lacking. The overarching goal of our study is to use machine learning techniques to understand the relationships\namong different biomarkers and to establish a system-level model that can better describe the interactions among\nbiomarkers and provide superior diagnostic and prognostic information. In this pilot study, we use Bayesian network\n(BN) to analyze multimodal data from ADNI, including demographics, volumetric MRI, PET, genotypes, and\nneuropsychometric measurements and demonstrate our approach to have superior prediction accuracy.
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