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Progression along data-driven disease timelines is predictive of Alzheimer's disease in a population-based cohort
2021
NeuroImage
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Our results suggest that co-init DEBM trained on case-controlled data is generalizable to a population-based cohort setting and that progression along the disease timelines is predictive of the development ...
if progression along these disease timelines is predictive of AD. ...
Acknowledgement This study is part of the EuroPOND initiative, which is funded by the European Union 's Horizon 2020 research and innovation programme ...
doi:10.1016/j.neuroimage.2021.118233
pmid:34091030
fatcat:mwqpuzmi7ndn5ocawmrhmlcqlu
Cohort discovery and risk stratification for Alzheimer's disease: an electronic health record‐based approach
2020
Alzheimer s & Dementia Translational Research & Clinical Interventions
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Using EHR data from the University of Michigan (UM) hospitals and consensus-based diagnoses from the Michigan Alzheimer's Disease Research Center, we developed and validated a cohort discovery tool for ...
We sought to leverage data routinely collected in electronic health records (EHRs), with the goal of developing patient risk stratification tools for predicting risk of developing Alzheimer's disease ( ...
Each row represents a timeline for the respective dataset, and encounters are indicated with squares. Shading along the Michigan-ADRC timeline indicates consensus-based diagnoses. ...
doi:10.1002/trc2.12035
pmid:32548236
pmcid:PMC7293993
fatcat:mkc2yqw2qzds3bwr2d7isnnu6a
Statistical Disease Progression Modeling in Alzheimer Disease
2020
Frontiers in Big Data
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Maximum-likelihood estimation in these models induces a data-driven criterion for separating disease progression and baseline cognition. ...
along the continuous time progression of disease. ...
In this article, we propose a new approach to disease progression modeling that separates disease stage and deviations from the mean pattern in a fully data-driven manner. ...
doi:10.3389/fdata.2020.00024
pmid:33693397
pmcid:PMC7931952
fatcat:jrhvrc6wp5a4jgru5cje7vnunq
A Vertex Clustering Model for Disease Progression: Application to Cortical Thickness Images
[chapter]
2017
Lecture Notes in Computer Science
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We present a disease progression model with single vertex resolution that we apply to cortical thickness data. ...
Moreover, our clustering model finds similar patterns of atrophy for typical Alzheimer's disease (tAD) subjects on two independent datasets: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and a ...
A hypothetical model of disease progression has been proposed by [1] , describing the trajectory of key biomarkers along the progression of Alzheimer's disease. ...
doi:10.1007/978-3-319-59050-9_11
fatcat:nkxw7qrlfffrfesdgobwjqcnxa
Modelling the Neuroanatomical Progression of Alzheimer's Disease and Posterior Cortical Atrophy
[article]
2020
arXiv
pre-print
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In this work I developed novel models of disease progression and applied them to estimate the progression of Alzheimer's disease and Posterior Cortical atrophy, a rare neurodegenerative syndrome causing ...
In order to find effective treatments for Alzheimer's disease (AD), we need to identify subjects at risk of AD as early as possible. ...
Secondly, I'd also like to thank Alexandra Young and Neil Oxtoby for teaching me disease progression modelling, especially in the early years of my PhD. ...
arXiv:2003.04805v1
fatcat:a5gmy75lvnfnhbtvztqoxikukm
A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients
2021
Applied Sciences
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Methods: In this study, we apply a digital twin model based on a variational autoencoder to a population of patients who went on to experience an ischemic stroke. ...
Machine learning methods have been developed to predict the likelihood of a given event or classify patients into two or more diagnostic categories. ...
Covariance structure similarity is important for ensuring that trends of disease progression found in models generated with real data will result in the same trends of disease progression as those found ...
doi:10.3390/app11125576
fatcat:dswr52cl5nhp7nsexmf2mn3nsu
DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorders
2019
NeuroImage
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DIVE is an image-based disease progression model with single-vertex resolution, designed to reconstruct long-term patterns of brain pathology from short-term longitudinal data sets. ...
Here we present DIVE: Data-driven Inference of Vertexwise Evolution. ...
Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number ...
doi:10.1016/j.neuroimage.2019.02.053
pmid:30844504
fatcat:gxq53thcfjhm3ohvggrdbaxway
Application of the ATN classification scheme in a population without dementia: Findings from the EPAD cohort
2021
Alzheimer's & Dementia
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Age was 65 ± 7 years, with 58% females and 38% apolipoprotein E (APOE) ε4 carriers; 57.1% were A-T-N-, 32.5% were in the Alzheimer's disease (AD) continuum, and 10.4% suspected non-Alzheimer's pathology ...
Paradoxically higher regional gray matter volumes were observed in A+T-N- compared to A-T-N- (P < 0.001). In non-demented individuals along the AD continuum, p-tau drives cognitive dysfunction. ...
INTRODUCTION Finding disease-modifying therapies for Alzheimer's disease (AD), the most prevalent cause of dementia, is an international priority. ...
doi:10.1002/alz.12292
pmid:33811742
pmcid:PMC8359976
fatcat:kxc42eyqmnaq5dnhmy64klpcbe
Differences in topological progression profile among neurodegenerative diseases from imaging data
2019
eLife
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We introduce the notion of a topological profile — a characteristic combination of topological descriptors that best describes the propagation of pathology in a particular disease. ...
By drawing on recent advances in disease progression modeling, we estimate topological profiles from the full course of pathology accumulation, at both cohort and individual levels. ...
model of mechanistic brain Atrophy Propagation in Dementia'. ...
doi:10.7554/elife.49298
pmid:31793876
pmcid:PMC6922631
fatcat:yyornpu2jnhcnm53t5eavctnje
A Precision Medicine Initiative for Alzheimer's disease: the road ahead to biomarker-guided integrative disease modeling
2017
Climacteric
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patient in a different way; (V) turning descriptive scenarios of disease progression into predictive systems. ...
The integration of spatio-temporal measurements into a digital model of disease progression is often based on the idea of regressing measurements against an estimated time to disease onset 80, 81 . ...
doi:10.1080/13697137.2017.1287866
pmid:28286989
fatcat:5bdwpvg4jbej7ba3tkmrqw3keq
Quantifying Neurodegenerative Progression With DeepSymNet, an End-to-End Data-Driven Approach
2019
Frontiers in Neuroscience
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Alzheimer's disease (AD) is the most common neurodegenerative disorder worldwide and is one of the leading sources of morbidity and mortality in the aging population. ...
Here we propose a data-driven method based on an extension of a deep learning architecture, DeepSymNet, that identifies longitudinal changes without relying on prior brain regions of interest, an atlas ...
We found that the INTRODUCTION Alzheimer's disease (AD) is the leading cause of dementia globally (50-75%) and is distinguished by a progressive cognitive decline (Lane et al., 2018) . ...
doi:10.3389/fnins.2019.01053
pmid:31636533
pmcid:PMC6788344
fatcat:fow6zw4egne5pc23xdzzwezxbm
Proteomics Landscape of Alzheimer's Disease
2021
Proteomes
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Alzheimer's disease (AD) is the most prevalent form of dementia, and the numbers of AD patients are expected to increase as human life expectancy improves. ...
Deposition of β-amyloid protein (Aβ) in the extracellular matrix and intracellular neurofibrillary tangles are molecular hallmarks of the disease. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/proteomes9010013
pmid:33801961
fatcat:n4xy432lljdo5dx64oy6ealuhi
The PredictAD project: development of novel biomarkers and analysis software for early diagnosis of the Alzheimer's disease
2013
Interface Focus
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One contribution of 25 to a Theme Issue 'The virtual physiological human: integrative approaches to computational biomedicine'. ...
The project provided several novel tools for biomarker discovery and a novel data-driven and evidence-based disease profiling. ...
Introduction Alzheimer's disease (AD) is the most common cause of dementia. ...
doi:10.1098/rsfs.2012.0072
pmid:24427524
pmcid:PMC3638476
fatcat:v5qefbq7wbgmxpjlz5njxhqky4
Deep representation learning of electronic health records to unlock patient stratification at scale
2020
npj Digital Medicine
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However, challenges in summarizing and representing patient data prevent widespread practice of scalable EHR-based stratification analysis. ...
We considered EHRs of 1,608,741 patients from a diverse hospital cohort comprising a total of 57,464 clinical concepts. ...
ACKNOWLEDGEMENTS R.M. would like to thank the support from the Hasso Plattner Foundation, the Alzheimer's Drug Discovery Foundation and a courtesy GPU donation from Nvidia. ...
doi:10.1038/s41746-020-0301-z
pmid:32699826
pmcid:PMC7367859
fatcat:ddt7xa36jvbzzdkpirhdslxnty
DPVis: Visual Exploration of Disease Progression Pathways
[article]
2019
arXiv
pre-print
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In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and designing ...
One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. ...
ACKNOWLEDGMENTS We wish to thank the T1DI Study Group for their help in this work. ...
arXiv:1904.11652v1
fatcat:am3ei3niabbinkobr7v5yqbhoe
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