Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy

Abstract Osteoporosis is a global health problem for ageing populations. The goals of osteoporosis treatment are to improve bone mineral density (BMD) and prevent fractures. One major obstacle that remains a great challenge to achieve the goals is how to select the best treatment regimen for individ...

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Autores principales:	Thiraphat Tanphiriyakun, Sattaya Rojanasthien, Piyapong Khumrin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/13822a6440b04fa3a47f77a537e9bbf1
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spelling	oai:doaj.org-article:13822a6440b04fa3a47f77a537e9bbf12021-12-02T18:34:06ZBone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy10.1038/s41598-021-93152-52045-2322https://doaj.org/article/13822a6440b04fa3a47f77a537e9bbf12021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93152-5https://doaj.org/toc/2045-2322Abstract Osteoporosis is a global health problem for ageing populations. The goals of osteoporosis treatment are to improve bone mineral density (BMD) and prevent fractures. One major obstacle that remains a great challenge to achieve the goals is how to select the best treatment regimen for individual patients. We developed a computational model from 8981 clinical variables, including demographic data, diagnoses, laboratory results, medications, and initial BMD results, taken from 10-year period of electronic medical records to predict BMD response after treatment. We trained 7 machine learning models with 13,562 osteoporosis treatment instances [comprising 5080 (37.46%) inadequate treatment responses and 8482 (62.54%) adequate responses] and selected the best model (Random Forests with area under the receiver operating curve of 0.70, accuracy of 0.69, precision of 0.70, and recall of 0.89) to individually predict treatment responses of 11 therapeutic regimens, then selected the best predicted regimen to compare with the actual regimen. The results showed that the average treatment response of the recommended regimens was 9.54% higher than the actual regimens. In summary, our novel approach using a machine learning-based decision support system is capable of predicting BMD response after osteoporosis treatment and personalising the most appropriate treatment regimen for an individual patient.Thiraphat TanphiriyakunSattaya RojanasthienPiyapong KhumrinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Thiraphat Tanphiriyakun Sattaya Rojanasthien Piyapong Khumrin Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy
description	Abstract Osteoporosis is a global health problem for ageing populations. The goals of osteoporosis treatment are to improve bone mineral density (BMD) and prevent fractures. One major obstacle that remains a great challenge to achieve the goals is how to select the best treatment regimen for individual patients. We developed a computational model from 8981 clinical variables, including demographic data, diagnoses, laboratory results, medications, and initial BMD results, taken from 10-year period of electronic medical records to predict BMD response after treatment. We trained 7 machine learning models with 13,562 osteoporosis treatment instances [comprising 5080 (37.46%) inadequate treatment responses and 8482 (62.54%) adequate responses] and selected the best model (Random Forests with area under the receiver operating curve of 0.70, accuracy of 0.69, precision of 0.70, and recall of 0.89) to individually predict treatment responses of 11 therapeutic regimens, then selected the best predicted regimen to compare with the actual regimen. The results showed that the average treatment response of the recommended regimens was 9.54% higher than the actual regimens. In summary, our novel approach using a machine learning-based decision support system is capable of predicting BMD response after osteoporosis treatment and personalising the most appropriate treatment regimen for an individual patient.
format	article
author	Thiraphat Tanphiriyakun Sattaya Rojanasthien Piyapong Khumrin
author_facet	Thiraphat Tanphiriyakun Sattaya Rojanasthien Piyapong Khumrin
author_sort	Thiraphat Tanphiriyakun
title	Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy
title_short	Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy
title_full	Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy
title_fullStr	Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy
title_full_unstemmed	Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy
title_sort	bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/13822a6440b04fa3a47f77a537e9bbf1
work_keys_str_mv	AT thiraphattanphiriyakun bonemineraldensityresponsepredictionfollowingosteoporosistreatmentusingmachinelearningtoaidpersonalizedtherapy AT sattayarojanasthien bonemineraldensityresponsepredictionfollowingosteoporosistreatmentusingmachinelearningtoaidpersonalizedtherapy AT piyapongkhumrin bonemineraldensityresponsepredictionfollowingosteoporosistreatmentusingmachinelearningtoaidpersonalizedtherapy
_version_	1718377903920513024

Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy

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