Machine learning methods to predict amyloid positivity using domain scores from cognitive tests

Abstract Amyloid- $$\beta$$ β (A $$\beta$$ β ) is the target in many clinical trials for Alzheimer’s disease (AD). Preclinical AD patients are heterogeneous with regards to different backgrounds and diagnosis. Accurately predicting A $$\beta$$ β status of participants by using machine learning (ML)...

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Autores principales:	Guogen Shan, Charles Bernick, Jessica Z. K. Caldwell, Aaron Ritter
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ea894322e2e844929f1099088cdfa50c
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spelling	oai:doaj.org-article:ea894322e2e844929f1099088cdfa50c2021-12-02T13:20:02ZMachine learning methods to predict amyloid positivity using domain scores from cognitive tests10.1038/s41598-021-83911-92045-2322https://doaj.org/article/ea894322e2e844929f1099088cdfa50c2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83911-9https://doaj.org/toc/2045-2322Abstract Amyloid- $$\beta$$ β (A $$\beta$$ β ) is the target in many clinical trials for Alzheimer’s disease (AD). Preclinical AD patients are heterogeneous with regards to different backgrounds and diagnosis. Accurately predicting A $$\beta$$ β status of participants by using machine learning (ML) models based on easily accessible data, could improve the effectiveness of AD clinical trials. We will develop optimal ML models for each subpopulation stratified by sex and disease stages using sub scores from screening neurological tests. Data from the AD Neuroimaging Initiative (ADNI) were used to build the ML models, for three groups: individuals with significant memory concern, early mild cognitive impairment (MCI), and late MCI. Data were further separated into 6 groups by disease stage (3 levels) and sex (2 categories). The outcome was defined as the A $$\beta$$ β status confirmed by the PET imaging, and the features include demographic data, newly identified risk factors, screening tests, and the domain scores from screening tests. Monte Carlo simulation studies were used together with k-fold cross-validation technique to compute model performance metric. We also develop a new feature selection method based on the stochastic ordering to avoiding searching all possible combinations of features. Accuracy of the identified optimal model for SMC male was over 90% by using domain scores, and accuracy for LMCI female was above 86%. Domain scores can improve the ML model prediction as compared to the total scores. Accurate ML prediction models can identify the proper population for AD clinical trials.Guogen ShanCharles BernickJessica Z. K. CaldwellAaron RitterNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Guogen Shan Charles Bernick Jessica Z. K. Caldwell Aaron Ritter Machine learning methods to predict amyloid positivity using domain scores from cognitive tests
description	Abstract Amyloid- $$\beta$$ β (A $$\beta$$ β ) is the target in many clinical trials for Alzheimer’s disease (AD). Preclinical AD patients are heterogeneous with regards to different backgrounds and diagnosis. Accurately predicting A $$\beta$$ β status of participants by using machine learning (ML) models based on easily accessible data, could improve the effectiveness of AD clinical trials. We will develop optimal ML models for each subpopulation stratified by sex and disease stages using sub scores from screening neurological tests. Data from the AD Neuroimaging Initiative (ADNI) were used to build the ML models, for three groups: individuals with significant memory concern, early mild cognitive impairment (MCI), and late MCI. Data were further separated into 6 groups by disease stage (3 levels) and sex (2 categories). The outcome was defined as the A $$\beta$$ β status confirmed by the PET imaging, and the features include demographic data, newly identified risk factors, screening tests, and the domain scores from screening tests. Monte Carlo simulation studies were used together with k-fold cross-validation technique to compute model performance metric. We also develop a new feature selection method based on the stochastic ordering to avoiding searching all possible combinations of features. Accuracy of the identified optimal model for SMC male was over 90% by using domain scores, and accuracy for LMCI female was above 86%. Domain scores can improve the ML model prediction as compared to the total scores. Accurate ML prediction models can identify the proper population for AD clinical trials.
format	article
author	Guogen Shan Charles Bernick Jessica Z. K. Caldwell Aaron Ritter
author_facet	Guogen Shan Charles Bernick Jessica Z. K. Caldwell Aaron Ritter
author_sort	Guogen Shan
title	Machine learning methods to predict amyloid positivity using domain scores from cognitive tests
title_short	Machine learning methods to predict amyloid positivity using domain scores from cognitive tests
title_full	Machine learning methods to predict amyloid positivity using domain scores from cognitive tests
title_fullStr	Machine learning methods to predict amyloid positivity using domain scores from cognitive tests
title_full_unstemmed	Machine learning methods to predict amyloid positivity using domain scores from cognitive tests
title_sort	machine learning methods to predict amyloid positivity using domain scores from cognitive tests
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ea894322e2e844929f1099088cdfa50c
work_keys_str_mv	AT guogenshan machinelearningmethodstopredictamyloidpositivityusingdomainscoresfromcognitivetests AT charlesbernick machinelearningmethodstopredictamyloidpositivityusingdomainscoresfromcognitivetests AT jessicazkcaldwell machinelearningmethodstopredictamyloidpositivityusingdomainscoresfromcognitivetests AT aaronritter machinelearningmethodstopredictamyloidpositivityusingdomainscoresfromcognitivetests
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Machine learning methods to predict amyloid positivity using domain scores from cognitive tests

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