Filters








2,350 Hits in 4.7 sec

Personalized Gaussian Processes for Future Prediction of Alzheimer's Disease Progression [article]

Kelly Peterson, Ognjen Rudovic, Ricardo Guerrero, Rosalind W. Picard
2018 arXiv   pre-print
In this paper, we introduce the use of a personalized Gaussian Process model (pGP) to predict the key metrics of Alzheimer's Disease progression (MMSE, ADAS-Cog13, CDRSB and CS) based on each patient's  ...  We show that this new approach, together with an auto-regressive formulation, leads to significant improvements in forecasting future clinical status and cognitive scores for target patients when compared  ...  In this work, we introduce a personalized Gaussian Process model (pGP) to predict the key metrics of the AD progression (MMSE, ADAS-Cog13, CDRSB and CS) based on each patient's previous visits.  ... 
arXiv:1712.00181v4 fatcat:7a6x76nrvbardim65f2u7r65hy

Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13) [article]

Yuria Utsumi, Ognjen Rudovic, Kelly Peterson, Ricardo Guerrero, Rosalind W. Picard
2018 arXiv   pre-print
In this paper, we introduce the use of a personalized Gaussian Process model (pGP) to predict per-patient changes in ADAS-Cog13 -- a significant predictor of Alzheimer's Disease (AD) in the cognitive domain  ...  We extend this personalized approach to predict the values of ADAS-Cog13 over the future 6, 12, 18, and 24 months.  ...  More specifically, we investigate the effects of the model personalization for the forecasting task using the framework of Gaussian Processes (GP) [8] .  ... 
arXiv:1802.08561v4 fatcat:3e6qnsvnlrdj7msifwednayzlm

Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels [article]

Alexander Lavin
2021 arXiv   pre-print
Our Bayesian approach combines the flexibility of Gaussian processes with the structural power of neural networks to model biomarker progressions, without needing clinical labels for training.  ...  We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases.  ...  We aim to predict the key cognitive decline metrics for Alzheimer's progression (MMSE, ADAS-Cog13, and CDRSB) corresponding to length-τ horizons into the future.  ... 
arXiv:2009.07738v3 fatcat:snzegvlnyzdn3fibg56wsurbki

A FRAMEWORK FOR PERFORMANCE EVALUATION OF MACHINE LEARNING TECHNIQUES TO PREDICT THE DECISION TO CHOOSE PALLIATIVE CARE IN ADVANCED STAGES OF ALZHEIMER'S DISEASE

Mutyala Sridevi, Arun Kumar B.R.
2021 Indian Journal of Computer Science and Engineering  
The lifestyle factors and behavioural traits play major role in the onset and progression of Alzheimer's disease compared to genetic factors.  ...  Alzheimer's is one of the chronic diseases that stand as a challenge in the geriatrics domain.  ...  The process of neuro-degeneration is affected by dysfunctionalities in neurotransmission along with personality traits and psychological factors playing an equally progressive role [12] .  ... 
doi:10.21817/indjcse/2021/v12i1/211201140 fatcat:4obiisib2vhqzcib2iye63xrou

Nonlinear biomarker interactions in conversion from mild cognitive impairment to Alzheimer's disease

Sebastian G. Popescu, Alex Whittington, Roger N. Gunn, Paul M. Matthews, Ben Glocker, David J Sharp, James H Cole, for the Alzheimer's Disease Neuroimaging Initiative
2020 Human Brain Mapping  
Here, we used Gaussian Processes to address this, modelling nonlinear interactions to predict progression from mild cognitive impairment (MCI) to Alzheimer's over 3 years, using Alzheimer's Disease Neuroimaging  ...  Multiple biomarkers can capture different facets of Alzheimer's disease.  ...  | DISCUSSION Here, we modelled nonlinear interactions between a panel of biomarkers (neuroimaging, genetic, CSF) to predict disease progression from MCI to Alzheimer's disease.  ... 
doi:10.1002/hbm.25133 pmid:32643852 pmcid:PMC7502835 fatcat:p45hw4myibb5fciqqrz5yrsq7y

Nonlinear biomarker interactions in conversion from Mild Cognitive Impairment to Alzheimer's disease [article]

Sebastian Popescu, Alex Whittington, Roger N Gunn, Paul M Matthews, Ben Glocker, David J Sharp, James H Cole
2019 medRxiv   pre-print
Here, we used Gaussian Processes to model nonlinear interactions when combining biomarkers to predict Alzheimer's disease conversion in 48 mild cognitive impairment participants who progressed to Alzheimer's  ...  The multi-faceted nature of Alzheimer's disease means that multiple biomarkers (e.g., amyloid-beta, tau, brain atrophy) may improve the tracking of disease progression and the prediction of health outcomes  ...  Acknowledgements Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging  ... 
doi:10.1101/19002378 fatcat:b3nhc3m6cjeatnlhxalqltlh6a

COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer's disease

Fan Zhu, Bharat Panwar, Hiroko H. Dodge, Hongdong Li, Benjamin M. Hampstead, Roger L. Albin, Henry L. Paulson, Yuanfang Guan
2016 Scientific Reports  
We present COMPASS, a COmputational Model to Predict the development of Alzheimer's diSease Spectrum, to model Alzheimer's disease (AD) progression.  ...  For (3), "genetic only" model has Pearson's correlation of 0.15 to predict progression in the MCI group.  ...  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:10.1038/srep34567 pmid:27703197 pmcid:PMC5050516 fatcat:nguewevtkbbsfn4gqxjplgkgaq

Automated Detection of Alzheimer's Disease Using Wavelet Transform with Convolutional Neural Networks

Nur OLABI, Aykut YILMAZ, Zafer ASLAN
2020 EURAS Journal of Health  
Individual computer aided systems are needed for early and accurate diagnosis of Alzheimer's.  ...  Objective: Alzheimer's disease (AD) is a chronic disease that causes the death of nerve cells and tissue loss in the brain. It usually starts slowly and worsens over time.  ...  It progresses in a variable way from person to person; It makes the life of patients very difficult and seriously reduces the quality of life (2) .  ... 
doi:10.17932/ejoh.2020.022/ejoh_v02i2004 fatcat:oavpk2nlmvcazfphgc5tvlyns4

Data-driven models of dominantly-inherited Alzheimer's disease progression

Neil P Oxtoby, Alexandra L Young, David M Cash, Tammie L S Benzinger, Anne M Fagan, John C Morris, Randall J Bateman, Nick C Fox, Jonathan M Schott, Daniel C Alexander
2018 Brain  
The models reveal hidden detail on dominantly-inherited Alzheimer's disease progression, as well as providing data-driven systems for fine-grained patient staging and prediction of symptom onset with great  ...  We use emerging techniques in generative data-driven disease progression modelling to characterize dominantly-inherited Alzheimer's disease progression with unprecedented resolution, and without relying  ...  Figure 6 : 6 Predicting onset of clinical symptoms. Figure 7 : 7 Summary: data-driven models of dominantly-inherited Alzheimer's disease progression.  ... 
doi:10.1093/brain/awy050 pmid:29579160 pmcid:PMC5920320 fatcat:eyc3r6mud5bo7nb4rmkb2qjpre

NormVAE: Normative Modeling on Neuroimaging Data using Variational Autoencoders [article]

Sayantan Kumar
2022 arXiv   pre-print
Next, we assess the trained model on Alzheimer's Disease (AD) patients to estimate how each disease patient deviated from the norm and identified the brain regions associated with the deviations.  ...  compared to baseline normative models like Gaussian Process Regression (GPR) and a standard VAE, which generates deterministic subject-level deviations without any uncertainty estimates.  ...  Abbreviations: ADAS13 = Alzheimer's Disease Assessment Scale; RAVLT = Rey Auditory Verbal Learning Test; VAE = Variational Autoencoder; GPR = Gaussian Process Regression.  ... 
arXiv:2110.04903v2 fatcat:fcjn6njvd5frlh4ol5gvqgjrxu

A Long Short-Term Memory Biomarker-Based Prediction Framework for Alzheimer's Disease

Anza Aqeel, Ali Hassan, Muhammad Attique Khan, Saad Rehman, Usman Tariq, Seifedine Kadry, Arnab Majumdar, Orawit Thinnukool
2022 Sensors  
The early prediction of Alzheimer's disease (AD) can be vital for the endurance of patients and establishes as an accommodating and facilitative factor for specialists.  ...  The proposed work presents a robotized predictive structure, dependent on machine learning (ML) methods for the forecast of AD.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s22041475 pmid:35214375 pmcid:PMC8874990 fatcat:pxlmcox3dfbflh3qii4c42hqdu

Data-driven models of dominantly-inherited Alzheimer's disease progression [article]

Neil P Oxtoby, Alexandra L Young, David M Cash, Tammie L S Benzinger, Anne M Fagan, John C Morris, Randall J Bateman, Nick C Fox, Jonathan M Schott, Daniel C Alexander
2018 biorxiv/medrxiv   pre-print
The models reveal hidden detail on dominantly-inherited Alzheimer's disease progression, as well as providing data-driven systems for fine-grained patient staging and prediction of symptom onset with great  ...  Dominantly-inherited Alzheimer's disease is widely hoped to hold the key to developing interventions for sporadic late onset Alzheimer's disease.  ...  The DIAN Expanded Registry welcomes contact from any families or treating clinicians interested in research about dominantly inherited familial Alzheimer's disease.  ... 
doi:10.1101/250654 fatcat:nks6gcvvtjcg3mqskr2eo7ohfe

Computational Modeling Studies of the Beta-Amyloid Protein Binding to Develop Drugs for the Treatment of Alzheimer's Disease

Jerry A Darsey
2017 Current Trends in Biomedical Engineering & Biosciences  
Alzheimer's Disease (AD) is a progressive neurodegenerative disease that affects memory and other mental functions due to the accumulation of Amyloid-Beta (Aβ) plaques.  ...  Then, each identified molecule was virtually modified and its new IC 50 value was determined utilizing Gaussian 09, a molecular modeling program, which was used to predict total energies.  ...  Sumreen Gul, a Ph.D. student, for help with running the Gaussian 09 software and giving one of the authors (ST) consistent feedback and tips throughout the process.  ... 
doi:10.19080/ctbeb.2017.10.555799 fatcat:kafuw6bjcjgh3pjjaa6bsowg7a

Investigating hypotheses of neurodegeneration by learning dynamical systems of protein propagation in the brain

Sara Garbarino, Marco Lorenzi
2021 NeuroImage  
as the best suited DS for bio-mechanical description of amyloid dynamics in AD, enabling realistic and accurate personalized simulation of amyloidosis.  ...  When applied to in-vivo AV45-PET brain imaging data measuring topographic amyloid deposition in Alzheimer's disease (AD), our model identified the mechanisms of accumulation, clearance and propagation  ...  Table 2 also reports RMSE for prediction for each disease status.  ... 
doi:10.1016/j.neuroimage.2021.117980 pmid:33823273 fatcat:3xutecez2jcpjg2t4twwbvqlxm

Predicting time to dementia using a quantitative template of disease progression

Murat Bilgel, Bruno M. Jedynak
2019 Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring  
Characterization of longitudinal trajectories of biomarkers implicated in sporadic Alzheimer's disease (AD) in decades before clinical diagnosis is important for disease prevention and monitoring.  ...  Our method provides a quantitative approach for characterizing the natural history of AD starting at preclinical stages despite the lack of individual-level longitudinal data spanning the entire disease  ...  Acknowledgments 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  ... 
doi:10.1016/j.dadm.2019.01.005 pmid:30859120 pmcid:PMC6396328 fatcat:iumrdkpve5clloaznuz7csufmu
« Previous Showing results 1 — 15 out of 2,350 results