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Predicting Disease Progression with a Model for Multivariate Longitudinal Clinical Data

Joseph Futoma, Mark Sendak, Blake Cameron, Katherine A. Heller
2016 Machine Learning in Health Care  
To this end, we propose a novel probabilistic generative model for multivariate longitudinal data that captures dependencies between multivariate trajectories of clinical variables.  ...  Our model's dynamic predictions have significantly lower errors compared to a recent state of the art method for modeling disease trajectories, and they are being incorporated into a population health  ...  Proposed Multivariate Disease Trajectory Model Our proposed hierarchical latent variable model jointly models each patient's multivariate longitudinal data by using a GP for each individual variable, with  ... 
dblp:conf/mlhc/FutomaSCH16 fatcat:dchpav5zpnexlddy3cu55ij6ai

Predicting the multi-domain progression of Parkinson's disease: a Bayesian multivariate generalized linear mixed-effect model

Ming Wang, Zheng Li, Eun Young Lee, Mechelle M. Lewis, Lijun Zhang, Nicholas W. Sterling, Daymond Wagner, Paul Eslinger, Guangwei Du, Xuemei Huang
2017 BMC Medical Research Methodology  
It is challenging for current statistical models to predict clinical progression of Parkinson's disease (PD) because of the involvement of multi-domains and longitudinal data.  ...  Conclusions: Multivariate general mixed models hold promise to predict clinical progression of individual outcomes in PD.  ...  Huang's work also was supported by the National Institute of Neurological Disease and Stroke (NS060722 and NS082151), the National Center for Advancing Translational Sciences (Grant UL1 TR000127 and TR002014  ... 
doi:10.1186/s12874-017-0415-4 pmid:28946857 pmcid:PMC5613469 fatcat:xosnrnpsmbg75cdlkydjezm2j4

A prognostic model of Alzheimer's disease relying on multiple longitudinal measures and time-to-event data

Kan Li, Richard O'Brien, Michael Lutz, Sheng Luo
2018 Alzheimer's & Dementia  
The objective was to develop a prognostic model, based on multivariate longitudinal makers, for predicting progression-free survival in patients with mild cognitive impairment.  ...  DISCUSSION-The prognostic model was improved by incorporating multiple longitudinal makers. It is useful for monitoring disease and identifying patients for clinical trial recruitment.  ...  Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense  ... 
doi:10.1016/j.jalz.2017.11.004 pmid:29306668 pmcid:PMC5938096 fatcat:bnfoaebrtbccjixseoffbnwr24

Multivariate joint modeling to identify markers of growth and lung function decline that predict cystic fibrosis pulmonary exacerbation onset

E. R. Andrinopoulou, J. P. Clancy, R. D. Szczesniak
2020 BMC Pulmonary Medicine  
Individualized dynamic predictions from joint modeling could enhance physician monitoring of CF disease progression by providing PE risk assessment over a patient's clinical course.  ...  its predictive performance with a class of multivariate joint models that included combinations of growth markers as additional submodels.  ...  Keywords: Dynamic prediction, Functional data analysis, Medical monitoring, Multivariate longitudinal data, Registry analysis, Time-to-event Background Cystic fibrosis (CF) is a chronic lung disease  ... 
doi:10.1186/s12890-020-1177-z pmid:32429862 fatcat:5lkbldjuz5dwjg4qt4rrgcz35i

Scalable Modeling of Multivariate Longitudinal Data for Prediction of Chronic Kidney Disease Progression [article]

Joseph Futoma, Mark Sendak, C. Blake Cameron, Katherine Heller
2016 arXiv   pre-print
To this end, we propose a novel probabilistic generative model for multivariate longitudinal data that captures dependencies between multivariate trajectories.  ...  Even when such a clinical variable exists, there are often additional related biomarkers routinely measured for patients that may better inform the predictions of their future disease state.  ...  Proposed Multivariate Disease Trajectory Model Our proposed hierarchical latent variable model jointly models each patient's multivariate longitudinal data by using a GP for each individual variable, with  ... 
arXiv:1608.04615v1 fatcat:qf4nvs2lzvb2nejukjc3krmkte

Can MRI-based multivariate gray matter volumetric distance predict motor progression and classify slow versus fast progressors in Parkinsons disease? [article]

Anupa Ambili Vijayakumari, Hubert H Fernandez, Benjamin L Walter
2022 medRxiv   pre-print
We developed a patient-specific multivariate gray matter volumetric distance using Mahalanobis distance (MGMV) to investigate the changes in MGMV over time using longitudinal linear mixed-effect model,  ...  Methods The study included 59 patients with PD (n = 40 for the primary analysis, 19 for the validation analysis), and 55 healthy controls with structural MRI from the Parkinson's Progression Markers Initiative  ...  Fox Foundation for Parkinson's Research (MJFF) with funding partners 4D Pharma, Abbvie, Acurex Therapeutics, Allergan, Amathus Therapeutics, ASAP, Avid Radiopharmaceuticals, Bial Biotech, Biogen, BioLegend  ... 
doi:10.1101/2022.07.25.22278012 fatcat:4go2rljzo5ez3m72ggg2vpcjdu

Does C-Reactive Protein Predict the Long-Term Progression of Interstitial Lung Disease and Survival in Patients With Early Systemic Sclerosis?

Xiaochun Liu, Maureen D. Mayes, Claudia Pedroza, Hilda T. Draeger, Emilio B. Gonzalez, Brock E. Harper, John D. Reveille, Shervin Assassi
2013 Arthritis care & research  
The predictive significance of CRP level was investigated by a joint analysis of longitudinal measurements (serial FVCs up to 13 years) and survival data.  ...  We examined the predictive significance of CRP level for long-term ILD progression in a large early SSc cohort. Methods.  ...  Assassi had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.  ... 
doi:10.1002/acr.21968 pmid:23401350 pmcid:PMC3816494 fatcat:gbdzyrjc35c6hk4iev3hm6lvgm

Deep Recurrent Model for Individualized Prediction of Alzheimer's Disease Progression

Wonsik Jung, Eunji Jun, Heung-Il Suk, Alzheimer's Disease Neuroimaging Initiative
2021 NeuroImage  
While many of the previous works considered cross-sectional analysis, more recent studies have focused on the diagnosis and prognosis of AD with longitudinal or time series data in a way of disease progression  ...  score, and (iv) clinical status prediction of a subject based on his/her longitudinal imaging biomarkers.  ...  Proposed method In this work, we propose a novel framework for AD progression modeling with incomplete longitudinal data.  ... 
doi:10.1016/j.neuroimage.2021.118143 pmid:33991694 fatcat:nrepyo7455crliosj4y454doc4

Quantitative longitudinal predictions of Alzheimer's disease by multi-modal predictive learning [article]

Mithilesh Prakash, Mahmoud Abdelaziz, Linda Zhang, Bryan A Strange, Jussi Tohka
2020 bioRxiv   pre-print
We hypothesize that multi-modal data and predictive learning models can be employed for longitudinally predicting ADAS-cog scores.  ...  Quantitatively predicting the progression of Alzheimers disease (AD) in an individual on a continuous scale, such as AD assessment scale-cognitive (ADAS-cog) scores, is informative for a personalized approach  ...  Acknowledgments This study was funded by the Research Committee of the Kuopio University Hospital Catchment Area for the State Research Funding (5041778) and The Finnish Foundation for Technology Promotion  ... 
doi:10.1101/2020.06.04.133645 fatcat:vjprnlyp7nfxtet6xecaljzwxa

Deep learning for clustering of multivariate clinical patient trajectories with missing values

Johann de Jong, Mohammad Asif Emon, Ping Wu, Reagon Karki, Meemansa Sood, Patrice Godard, Ashar Ahmad, Henri Vrooman, Martin Hofmann-Apitius, Holger Fröhlich
2019 GigaScience  
We then used VaDER to successfully stratify patients with Alzheimer disease and patients with Parkinson disease into subgroups characterized by clinically divergent disease progression profiles.  ...  VaDER relies on a Gaussian mixture variational autoencoder framework, which is further extended to (i) model multivariate time series and (ii) directly deal with missing values.  ...  patients with severely progressing disease.  ... 
doi:10.1093/gigascience/giz134 pmid:31730697 pmcid:PMC6857688 fatcat:ve5zye2mabecvhszp67bcmc3k4

Predicting clinical events using Bayesian multivariate linear mixed models with application to scleroderma

Ji Soo Kim, Ami A. Shah, Laura K. Hummers, Scott L. Zeger
2021 BMC Medical Research Methodology  
As additional data are observed during a patient's visit, the algorithm sequentially produces updated predictions for the future longitudinal trajectories and for ILD, cardiomyopathy, and PH.  ...  Background Scleroderma is a serious chronic autoimmune disease in which a patient's disease state manifests in several irregularly spaced longitudinal measures of lung, heart, skin, and other organ systems  ...  Hence, we also develop a cross-validated, sequential prediction algorithm (CVSP) for multivariate longitudinal data that combines the outputs from prior model runs with new data for the patient at hand  ... 
doi:10.1186/s12874-021-01439-y pmid:34773969 pmcid:PMC8590788 fatcat:cajqyvc3kzhadlfa2vn2alvp3i

Bayesian Multivariate Growth Mixture Modeling of Longitudinal Data: An Application to Alzheimer's Disease Study [article]

Wenyi Lin, Michael C Donohue, Philip Insel, Armin Schwartzman, Wesley K Thompson
2021 bioRxiv   pre-print
We propose a fexible Bayesian multivariate growth mixture model to identify distinct longitudinal patterns of data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study.  ...  Alzheimer's disease (AD) studies often collect longitudinal biomarker measures of multiple cohorts at different stages of disease and follow these biomarkers with a relatively short period of time.  ...  Data analysis Discussion We developed a Bayesian multiviariate growth mixture model for multivariate longitudinal data.  ... 
doi:10.1101/2021.03.10.434854 fatcat:t3n6xeqxu5fyphfp5o4mwwn624

Predicting the Risk of Huntington's Disease with Multiple Longitudinal Biomarkers

Fan Li, Kan Li, Cai Li, the PREDICT-HD and ENROLL-HD Investigators of the Huntington Study Group
2019 Journal of Huntington's Disease  
Huntington's disease (HD) has gradually become a public health threat, and there is a growing interest in developing prognostic models to predict the time for HD diagnosis.  ...  [5] used the Neurobiological Predictors of Huntington's Disease (PREDICT-HD) data to develop a joint model and evaluated the predictive performance of each marker for HD diagnosis.  ...  Both PREDICT-HD and ENROLL-HD collected a range of clinical data from participants.  ... 
doi:10.3233/jhd-190345 pmid:31256145 pmcid:PMC6718328 fatcat:oxhicp7ssjc2jon4kudqyjrkw4

Deep Recurrent Model for Individualized Prediction of Alzheimer's Disease Progression [article]

Wonsik Jung, Eunji Jun, Heung-Il Suk
2020 arXiv   pre-print
While many of the previous works considered cross-sectional analysis, more recent studies have focused on the diagnosis and prognosis of AD with longitudinal or time series data in a way of disease progression  ...  score, and (iv) clinical status prediction of a subject based on his/her longitudinal imaging biomarkers.  ...  We used the TADPOLE longitudinal cohort (https://tadpole.grand-challenge. org/Data/) from the publicly available ADNI database, including ADNI-1, ADNI-2 and ADNI-GO.  ... 
arXiv:2005.02643v2 fatcat:2vk3xjxp4vezveuovyi3npuxri

Demographic and clinical predictors of progression and mortality in connective tissue disease-associated interstitial lung disease: a retrospective cohort study

Chrystal Chan, Christopher J. Ryerson, James V. Dunne, Pearce G. Wilcox
2019 BMC Pulmonary Medicine  
Further longitudinal studies may add to current clinical prediction models for prognostication in CTD-ILD.  ...  The objective of this study was to identify baseline demographic and clinical characteristics that are associated with progression and mortality in CTD-ILD.  ...  Acknowledgements The authors would like to thank Fran Schooley for assistance in data acquisition, Terry Lee and the Centre for Health Evaluation and Outcome Sciences for statistical support, and the British  ... 
doi:10.1186/s12890-019-0943-2 pmid:31672127 pmcid:PMC6824100 fatcat:5leiqawvybdt3cu23vgulexlzu
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