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Data-driven causal model discovery and personalized prediction in Alzheimer's disease

With the explosive growth of biomarker data in Alzheimer’s disease (AD) clinical trials, numerous mathematical models have been developed to characterize disease-relevant biomarker trajectories over time. While some of these models are purely empiric, others are causal, built upon various hypotheses...

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Autores principales: Zheng, Haoyang, Petrella, Jeffrey R., Doraiswamy, P. Murali, Lin, Guang, Hao, Wenrui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9458727/
https://www.ncbi.nlm.nih.gov/pubmed/36076010
http://dx.doi.org/10.1038/s41746-022-00632-7
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author Zheng, Haoyang
Petrella, Jeffrey R.
Doraiswamy, P. Murali
Lin, Guang
Hao, Wenrui
author_facet Zheng, Haoyang
Petrella, Jeffrey R.
Doraiswamy, P. Murali
Lin, Guang
Hao, Wenrui
author_sort Zheng, Haoyang
collection PubMed
description With the explosive growth of biomarker data in Alzheimer’s disease (AD) clinical trials, numerous mathematical models have been developed to characterize disease-relevant biomarker trajectories over time. While some of these models are purely empiric, others are causal, built upon various hypotheses of AD pathophysiology, a complex and incompletely understood area of research. One of the most challenging problems in computational causal modeling is using a purely data-driven approach to derive the model’s parameters and the mathematical model itself, without any prior hypothesis bias. In this paper, we develop an innovative data-driven modeling approach to build and parameterize a causal model to characterize the trajectories of AD biomarkers. This approach integrates causal model learning, population parameterization, parameter sensitivity analysis, and personalized prediction. By applying this integrated approach to a large multicenter database of AD biomarkers, the Alzheimer’s Disease Neuroimaging Initiative, several causal models for different AD stages are revealed. In addition, personalized models for each subject are calibrated and provide accurate predictions of future cognitive status.
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spelling pubmed-94587272022-09-10 Data-driven causal model discovery and personalized prediction in Alzheimer's disease Zheng, Haoyang Petrella, Jeffrey R. Doraiswamy, P. Murali Lin, Guang Hao, Wenrui NPJ Digit Med Article With the explosive growth of biomarker data in Alzheimer’s disease (AD) clinical trials, numerous mathematical models have been developed to characterize disease-relevant biomarker trajectories over time. While some of these models are purely empiric, others are causal, built upon various hypotheses of AD pathophysiology, a complex and incompletely understood area of research. One of the most challenging problems in computational causal modeling is using a purely data-driven approach to derive the model’s parameters and the mathematical model itself, without any prior hypothesis bias. In this paper, we develop an innovative data-driven modeling approach to build and parameterize a causal model to characterize the trajectories of AD biomarkers. This approach integrates causal model learning, population parameterization, parameter sensitivity analysis, and personalized prediction. By applying this integrated approach to a large multicenter database of AD biomarkers, the Alzheimer’s Disease Neuroimaging Initiative, several causal models for different AD stages are revealed. In addition, personalized models for each subject are calibrated and provide accurate predictions of future cognitive status. Nature Publishing Group UK 2022-09-08 /pmc/articles/PMC9458727/ /pubmed/36076010 http://dx.doi.org/10.1038/s41746-022-00632-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Zheng, Haoyang
Petrella, Jeffrey R.
Doraiswamy, P. Murali
Lin, Guang
Hao, Wenrui
Data-driven causal model discovery and personalized prediction in Alzheimer's disease
title Data-driven causal model discovery and personalized prediction in Alzheimer's disease
title_full Data-driven causal model discovery and personalized prediction in Alzheimer's disease
title_fullStr Data-driven causal model discovery and personalized prediction in Alzheimer's disease
title_full_unstemmed Data-driven causal model discovery and personalized prediction in Alzheimer's disease
title_short Data-driven causal model discovery and personalized prediction in Alzheimer's disease
title_sort data-driven causal model discovery and personalized prediction in alzheimer's disease
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9458727/
https://www.ncbi.nlm.nih.gov/pubmed/36076010
http://dx.doi.org/10.1038/s41746-022-00632-7
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