Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

Abstract Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzhei...

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Autores principales:	Ji Hwan Park, Han Eol Cho, Jong Hun Kim, Melanie M. Wall, Yaakov Stern, Hyunsun Lim, Shinjae Yoo, Hyoung Seop Kim, Jiook Cha
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/97d73b0105684ab094538dfc73a2f603
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spelling	oai:doaj.org-article:97d73b0105684ab094538dfc73a2f6032021-12-02T14:02:55ZMachine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data10.1038/s41746-020-0256-02398-6352https://doaj.org/article/97d73b0105684ab094538dfc73a2f6032020-03-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0256-0https://doaj.org/toc/2398-6352Abstract Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: “definite AD” with diagnostic codes and dementia medication (n = 614) and “probable AD” with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.Ji Hwan ParkHan Eol ChoJong Hun KimMelanie M. WallYaakov SternHyunsun LimShinjae YooHyoung Seop KimJiook ChaNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-7 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Ji Hwan Park Han Eol Cho Jong Hun Kim Melanie M. Wall Yaakov Stern Hyunsun Lim Shinjae Yoo Hyoung Seop Kim Jiook Cha Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data
description	Abstract Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: “definite AD” with diagnostic codes and dementia medication (n = 614) and “probable AD” with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.
format	article
author	Ji Hwan Park Han Eol Cho Jong Hun Kim Melanie M. Wall Yaakov Stern Hyunsun Lim Shinjae Yoo Hyoung Seop Kim Jiook Cha
author_facet	Ji Hwan Park Han Eol Cho Jong Hun Kim Melanie M. Wall Yaakov Stern Hyunsun Lim Shinjae Yoo Hyoung Seop Kim Jiook Cha
author_sort	Ji Hwan Park
title	Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data
title_short	Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data
title_full	Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data
title_fullStr	Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data
title_full_unstemmed	Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data
title_sort	machine learning prediction of incidence of alzheimer’s disease using large-scale administrative health data
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/97d73b0105684ab094538dfc73a2f603
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Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

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