Predicting cognitive impairment in outpatients with epilepsy using machine learning techniques

Abstract Many studies report predictions for cognitive function but there are few predictions in epileptic patients; therefore, we established a workflow to efficiently predict outcomes of both the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) in outpatients with epil...

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Autores principales:	Feng Lin, Jiarui Han, Teng Xue, Jilan Lin, Shenggen Chen, Chaofeng Zhu, Han Lin, Xianyang Chen, Wanhui Lin, Huapin Huang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d06ade1ce43f4535aa529274a46757e4
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spelling	oai:doaj.org-article:d06ade1ce43f4535aa529274a46757e42021-12-02T17:13:17ZPredicting cognitive impairment in outpatients with epilepsy using machine learning techniques10.1038/s41598-021-99506-32045-2322https://doaj.org/article/d06ade1ce43f4535aa529274a46757e42021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99506-3https://doaj.org/toc/2045-2322Abstract Many studies report predictions for cognitive function but there are few predictions in epileptic patients; therefore, we established a workflow to efficiently predict outcomes of both the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) in outpatients with epilepsy. Data from 441 outpatients with epilepsy were included; of these, 433 patients met the 12 clinical characteristic criteria and were divided into training (n = 304) and experimental (n = 129) groups. After descriptive statistics were analyzed, cross-validation was used to select the optimal model. The random forest (RF) algorithm was combined with the redundancy analysis (RDA) algorithm; then, optimal feature selection and resampling were carried out after removing linear redundancy information. The features that contributed more to multiple outcomes were selected. Finally, the external traceability of the model was evaluated using the follow-up data. The RF algorithm was the best prediction model for both MMSE and MoCA outcomes. Finally, seven markers were screened by overlapping the top ten important features for MMSE ranked by RF modeling, those ranked for MoCA ranked by RF modeling, and those for both assessments ranked by RDA. The optimal combination of features were namely, sex, age, age of onset, seizure frequency, brain MRI abnormalities, epileptiform discharge in EEG and usage of drugs. which was the most efficient in predicting outcomes of MMSE, MoCA, and both assessments.Feng LinJiarui HanTeng XueJilan LinShenggen ChenChaofeng ZhuHan LinXianyang ChenWanhui LinHuapin HuangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Feng Lin Jiarui Han Teng Xue Jilan Lin Shenggen Chen Chaofeng Zhu Han Lin Xianyang Chen Wanhui Lin Huapin Huang Predicting cognitive impairment in outpatients with epilepsy using machine learning techniques
description	Abstract Many studies report predictions for cognitive function but there are few predictions in epileptic patients; therefore, we established a workflow to efficiently predict outcomes of both the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) in outpatients with epilepsy. Data from 441 outpatients with epilepsy were included; of these, 433 patients met the 12 clinical characteristic criteria and were divided into training (n = 304) and experimental (n = 129) groups. After descriptive statistics were analyzed, cross-validation was used to select the optimal model. The random forest (RF) algorithm was combined with the redundancy analysis (RDA) algorithm; then, optimal feature selection and resampling were carried out after removing linear redundancy information. The features that contributed more to multiple outcomes were selected. Finally, the external traceability of the model was evaluated using the follow-up data. The RF algorithm was the best prediction model for both MMSE and MoCA outcomes. Finally, seven markers were screened by overlapping the top ten important features for MMSE ranked by RF modeling, those ranked for MoCA ranked by RF modeling, and those for both assessments ranked by RDA. The optimal combination of features were namely, sex, age, age of onset, seizure frequency, brain MRI abnormalities, epileptiform discharge in EEG and usage of drugs. which was the most efficient in predicting outcomes of MMSE, MoCA, and both assessments.
format	article
author	Feng Lin Jiarui Han Teng Xue Jilan Lin Shenggen Chen Chaofeng Zhu Han Lin Xianyang Chen Wanhui Lin Huapin Huang
author_facet	Feng Lin Jiarui Han Teng Xue Jilan Lin Shenggen Chen Chaofeng Zhu Han Lin Xianyang Chen Wanhui Lin Huapin Huang
author_sort	Feng Lin
title	Predicting cognitive impairment in outpatients with epilepsy using machine learning techniques
title_short	Predicting cognitive impairment in outpatients with epilepsy using machine learning techniques
title_full	Predicting cognitive impairment in outpatients with epilepsy using machine learning techniques
title_fullStr	Predicting cognitive impairment in outpatients with epilepsy using machine learning techniques
title_full_unstemmed	Predicting cognitive impairment in outpatients with epilepsy using machine learning techniques
title_sort	predicting cognitive impairment in outpatients with epilepsy using machine learning techniques
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d06ade1ce43f4535aa529274a46757e4
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Predicting cognitive impairment in outpatients with epilepsy using machine learning techniques

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