Unsupervised Machine Learning-Based Analysis of Clinical Features, Bone Mineral Density Features and Medical Care Costs of Rotator Cuff Tears

Unsupervised Machine Learning-Based Analysis of Clinical Features, Bone Mineral Density Features and Medical Care Costs of Rotator Cuff Tears

Tong-Fu Wang,* De-Sheng Chen,* Jia-wang Zhu, Bo Zhu, Zeng-Liang Wang, Jian-Gang Cao, Cai-Hong Feng, Jun-Wei Zhao Department of Sports Medicine and Arthroscopy, Tianjin Hospital of Tianjin University, Tianjin, People’s Republic of China*These authors contributed equally to thi...

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Main Authors: Wang TF, Chen DS, Zhu JW, Zhu B, Wang ZL, Cao JG, Feng CH, Zhao JW
Format: Article
Language:English
Published: Dove Medical Press 2021-09-01
Series:Risk Management and Healthcare Policy
Subjects:
unsupervised machine learning
clinical features
bone mineral density
medical care costs
rotator cuff tears.
Online Access:https://www.dovepress.com/unsupervised-machine-learning-based-analysis-of-clinical-features-bone-peer-reviewed-fulltext-article-RMHP
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spelling doaj-c38f6fc3a204439f9180dcde1ce008382021-09-23T19:56:27ZengDove Medical PressRisk Management and Healthcare Policy1179-15942021-09-01Volume 143977398669103Unsupervised Machine Learning-Based Analysis of Clinical Features, Bone Mineral Density Features and Medical Care Costs of Rotator Cuff TearsWang TFChen DSZhu JWZhu BWang ZLCao JGFeng CHZhao JWTong-Fu Wang,* De-Sheng Chen,* Jia-wang Zhu, Bo Zhu, Zeng-Liang Wang, Jian-Gang Cao, Cai-Hong Feng, Jun-Wei Zhao Department of Sports Medicine and Arthroscopy, Tianjin Hospital of Tianjin University, Tianjin, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jia-wang ZhuDepartment of Sports Medicine and Arthroscopy, Tianjin Hospital of Tianjin University, Tianjin, People’s Republic of ChinaEmail zhulingelou@126.comPurpose: We aim to present unsupervised machine learning-based analysis of clinical features, bone mineral density (BMD) features, and medical care costs of Rotator cuff tears (RCT).Patients and Methods: Fifty-three patients with RCT were reviewed, the clinical features, BMD features, and medical care costs were collected and analyzed by descriptive statistics. Furtherly, unsupervised machine learning (UML) algorithm was used for dimensionality reduction and cluster analysis of the RCT data.Results: There were 26 males and 27 females. The patients were divided into four subgroups using the UML algorithm. There were significant differences among four subgroups regarding trauma exposure, full-thickness supraspinatus tendon tears, infraspinatus tendon tear, subscapularis tendon tear, BMD distribution, medial row anchors, lateral row anchors, total medical care costs, and consumables costs. We observed the highest frequency of trauma exposure, infraspinatus tendon tear, subscapularis tendon tear, osteoporosis, the highest number of medial row anchors, lateral row anchors, total medical care costs, and consumables costs in subgroup II.Conclusion: The unsupervised machine learning-based analysis of RCT can provide clinically meaningful classification, which shows good interpretability and contribute to a better understanding of RCT. The significance of the results is limited due to the small number of samples, a larger follow-up study is needed to confirm the encouraging results.Keywords: unsupervised machine learning, clinical features, bone mineral density, medical care costs, rotator cuff tearshttps://www.dovepress.com/unsupervised-machine-learning-based-analysis-of-clinical-features-bone-peer-reviewed-fulltext-article-RMHPunsupervised machine learningclinical featuresbone mineral densitymedical care costsrotator cuff tears.
collection DOAJ
language English
format Article
sources DOAJ
author Wang TF
Chen DS
Zhu JW
Zhu B
Wang ZL
Cao JG
Feng CH
Zhao JW
spellingShingle Wang TF
Chen DS
Zhu JW
Zhu B
Wang ZL
Cao JG
Feng CH
Zhao JW
Unsupervised Machine Learning-Based Analysis of Clinical Features, Bone Mineral Density Features and Medical Care Costs of Rotator Cuff Tears
Risk Management and Healthcare Policy
unsupervised machine learning
clinical features
bone mineral density
medical care costs
rotator cuff tears.
author_facet Wang TF
Chen DS
Zhu JW
Zhu B
Wang ZL
Cao JG
Feng CH
Zhao JW
author_sort Wang TF
title Unsupervised Machine Learning-Based Analysis of Clinical Features, Bone Mineral Density Features and Medical Care Costs of Rotator Cuff Tears
title_short Unsupervised Machine Learning-Based Analysis of Clinical Features, Bone Mineral Density Features and Medical Care Costs of Rotator Cuff Tears
title_full Unsupervised Machine Learning-Based Analysis of Clinical Features, Bone Mineral Density Features and Medical Care Costs of Rotator Cuff Tears
title_fullStr Unsupervised Machine Learning-Based Analysis of Clinical Features, Bone Mineral Density Features and Medical Care Costs of Rotator Cuff Tears
title_full_unstemmed Unsupervised Machine Learning-Based Analysis of Clinical Features, Bone Mineral Density Features and Medical Care Costs of Rotator Cuff Tears
title_sort unsupervised machine learning-based analysis of clinical features, bone mineral density features and medical care costs of rotator cuff tears
publisher Dove Medical Press
series Risk Management and Healthcare Policy
issn 1179-1594
publishDate 2021-09-01
description Tong-Fu Wang,* De-Sheng Chen,* Jia-wang Zhu, Bo Zhu, Zeng-Liang Wang, Jian-Gang Cao, Cai-Hong Feng, Jun-Wei Zhao Department of Sports Medicine and Arthroscopy, Tianjin Hospital of Tianjin University, Tianjin, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jia-wang ZhuDepartment of Sports Medicine and Arthroscopy, Tianjin Hospital of Tianjin University, Tianjin, People’s Republic of ChinaEmail zhulingelou@126.comPurpose: We aim to present unsupervised machine learning-based analysis of clinical features, bone mineral density (BMD) features, and medical care costs of Rotator cuff tears (RCT).Patients and Methods: Fifty-three patients with RCT were reviewed, the clinical features, BMD features, and medical care costs were collected and analyzed by descriptive statistics. Furtherly, unsupervised machine learning (UML) algorithm was used for dimensionality reduction and cluster analysis of the RCT data.Results: There were 26 males and 27 females. The patients were divided into four subgroups using the UML algorithm. There were significant differences among four subgroups regarding trauma exposure, full-thickness supraspinatus tendon tears, infraspinatus tendon tear, subscapularis tendon tear, BMD distribution, medial row anchors, lateral row anchors, total medical care costs, and consumables costs. We observed the highest frequency of trauma exposure, infraspinatus tendon tear, subscapularis tendon tear, osteoporosis, the highest number of medial row anchors, lateral row anchors, total medical care costs, and consumables costs in subgroup II.Conclusion: The unsupervised machine learning-based analysis of RCT can provide clinically meaningful classification, which shows good interpretability and contribute to a better understanding of RCT. The significance of the results is limited due to the small number of samples, a larger follow-up study is needed to confirm the encouraging results.Keywords: unsupervised machine learning, clinical features, bone mineral density, medical care costs, rotator cuff tears
topic unsupervised machine learning
clinical features
bone mineral density
medical care costs
rotator cuff tears.
url https://www.dovepress.com/unsupervised-machine-learning-based-analysis-of-clinical-features-bone-peer-reviewed-fulltext-article-RMHP
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