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Personalized Gaussian Processes for Future Prediction of Alzheimer's Disease Progression
[article]
2018
arXiv
pre-print
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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]
2018
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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
2021
Indian Journal of Computer Science and Engineering
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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
2020
Human Brain Mapping
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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]
2019
medRxiv
pre-print
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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
2016
Scientific Reports
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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
2020
EURAS Journal of Health
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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
2018
Brain
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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]
2022
arXiv
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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
2022
Sensors
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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]
2018
biorxiv/medrxiv
pre-print
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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
2017
Current Trends in Biomedical Engineering & Biosciences
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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
2021
NeuroImage
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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
2019
Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
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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
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