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Disease Knowledge Transfer across Neurodegenerative Diseases

We introduce Disease Knowledge Transfer (DKT), a novel technique for transferring biomarker information between related neurodegenerative diseases. DKT infers robust multimodal biomarker trajectories in rare neurodegenerative diseases even when only limited, unimodal data is available, by transferring information from larger multimodal datasets from common neurodegenerative diseases. DKT is a joint-disease generative model of biomarker progressions, which exploits biomarker relationships that are shared across diseases. Our proposed method allows, for the first time, the estimation of plausible, multimodal biomarker trajectories in Posterior Cortical Atrophy (PCA), a rare neurodegenerative disease where only unimodal MRI data is available. For this we train DKT on a combined dataset containing subjects with two distinct diseases and sizes of data available: 1) a larger, multimodal typical AD (tAD) dataset from the TADPOLE Challenge, and 2) a smaller unimodal Posterior Cortical Atrophy (PCA) dataset from the Dementia Research Centre (DRC), for which only a limited number of Magnetic Resonance Imaging (MRI) scans are available. Although validation is challenging due to lack of data in PCA, we validate DKT on synthetic data and two patient datasets (TADPOLE and PCA cohorts), showing it can estimate the ground truth parameters in the simulation and predict unseen biomarkers on the two patient datasets. While we demonstrated DKT on Alzheimer's variants, we note DKT is generalisable to other forms of related neurodegenerative diseases. Source code for DKT is available online: https://github.com/mrazvan22/dkt.Comment: accepted at MICCAI 2019, 13 pages, 5 figures, 2 table

Trajectories of Disease Accumulation Using Electronic Health Records

Multimorbidity is a major problem for patients and health services. However, we still do not know much about the common trajectories of disease accumulation that patients follow. We apply a data-driven method to an electronic health record dataset (CPRD) to analyse and condense the main trajectories to multimorbidity into simple networks. This analysis has never been done specifically for multimorbidity trajectories and using primary care based electronic health records. We start the analysis by evaluating temporal correlations between diseases to determine which pairs of disease appear significantly in sequence. Then, we use patient trajectories together with the temporal correlations to build networks of disease accumulation. These networks are able to represent the main pathways that patients follow to acquire multiple chronic conditions. The first network that we find contains the common diseases that multimorbid patients suffer from and shows how diseases like diabetes, COPD, cancer and osteoporosis are crucial in the disease trajectories. The results we present can help better characterize multimorbid patients and highlight common combinations helping to focus treatment to prevent or delay multimorbidity progression