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A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record

PURPOSE: To develop deep learning model (Deep-KOA) that can predict the risk of knee osteoarthritis (KOA) within the next year by using the previous three years nonimage-based electronic medical record (EMR) data. PATIENTS AND METHODS: We randomly selected information of two million patients from th...

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Autores principales: Ningrum, Dina Nur Anggraini, Kung, Woon-Man, Tzeng, I-Shiang, Yuan, Sheng-Po, Wu, Chieh-Chen, Huang, Chu-Ya, Muhtar, Muhammad Solihuddin, Nguyen, Phung-Anh, Li, Jack Yu-Chuan, Wang, Yao-Chin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8445097/
https://www.ncbi.nlm.nih.gov/pubmed/34539180
http://dx.doi.org/10.2147/JMDH.S325179
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author Ningrum, Dina Nur Anggraini
Kung, Woon-Man
Tzeng, I-Shiang
Yuan, Sheng-Po
Wu, Chieh-Chen
Huang, Chu-Ya
Muhtar, Muhammad Solihuddin
Nguyen, Phung-Anh
Li, Jack Yu-Chuan
Wang, Yao-Chin
author_facet Ningrum, Dina Nur Anggraini
Kung, Woon-Man
Tzeng, I-Shiang
Yuan, Sheng-Po
Wu, Chieh-Chen
Huang, Chu-Ya
Muhtar, Muhammad Solihuddin
Nguyen, Phung-Anh
Li, Jack Yu-Chuan
Wang, Yao-Chin
author_sort Ningrum, Dina Nur Anggraini
collection PubMed
description PURPOSE: To develop deep learning model (Deep-KOA) that can predict the risk of knee osteoarthritis (KOA) within the next year by using the previous three years nonimage-based electronic medical record (EMR) data. PATIENTS AND METHODS: We randomly selected information of two million patients from the Taiwan National Health Insurance Research Database (NHIRD) from January 1, 1999 to December 31, 2013. During the study period, 132,594 patients were diagnosed with KOA, while 1,068,464 patients without KOA were chosen randomly as control. We constructed a feature matrix by using the three-year history of sequential diagnoses, drug prescriptions, age, and sex. Deep learning methods of convolutional neural network (CNN) and artificial neural network (ANN) were used together to develop a risk prediction model. We used the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and precision to evaluate the performance of Deep-KOA. Then, we explored the important features using stepwise feature selection. RESULTS: This study included 132,594 KOA patients, 83,111 females (62.68%), 49,483 males (37.32%), mean age 64.2 years, and 1,068,464 non-KOA patients, 545,902 females (51.09%), 522,562 males (48.91%), mean age 51.00 years. The Deep-KOA achieved an overall AUROC, sensitivity, specificity, and precision of 0.97, 0.89, 0.93, and 0.80 respectively. The discriminative analysis of Deep-KOA showed important features from several diseases such as disorders of the eye and adnexa, acute respiratory infection, other metabolic and immunity disorders, and diseases of the musculoskeletal and connective tissue. Age and sex were not found as the most discriminative features, with AUROC of 0.9593 (−0.76% loss) and 0.9644 (−0.25% loss) respectively. Whereas medications including antacid, cough suppressant, and expectorants were identified as discriminative features. CONCLUSION: Deep-KOA was developed to predict the risk of KOA within one year earlier, which may provide clues for clinical decision support systems to target patients with high risk of KOA to get precision prevention program.
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spelling pubmed-84450972021-09-17 A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record Ningrum, Dina Nur Anggraini Kung, Woon-Man Tzeng, I-Shiang Yuan, Sheng-Po Wu, Chieh-Chen Huang, Chu-Ya Muhtar, Muhammad Solihuddin Nguyen, Phung-Anh Li, Jack Yu-Chuan Wang, Yao-Chin J Multidiscip Healthc Original Research PURPOSE: To develop deep learning model (Deep-KOA) that can predict the risk of knee osteoarthritis (KOA) within the next year by using the previous three years nonimage-based electronic medical record (EMR) data. PATIENTS AND METHODS: We randomly selected information of two million patients from the Taiwan National Health Insurance Research Database (NHIRD) from January 1, 1999 to December 31, 2013. During the study period, 132,594 patients were diagnosed with KOA, while 1,068,464 patients without KOA were chosen randomly as control. We constructed a feature matrix by using the three-year history of sequential diagnoses, drug prescriptions, age, and sex. Deep learning methods of convolutional neural network (CNN) and artificial neural network (ANN) were used together to develop a risk prediction model. We used the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and precision to evaluate the performance of Deep-KOA. Then, we explored the important features using stepwise feature selection. RESULTS: This study included 132,594 KOA patients, 83,111 females (62.68%), 49,483 males (37.32%), mean age 64.2 years, and 1,068,464 non-KOA patients, 545,902 females (51.09%), 522,562 males (48.91%), mean age 51.00 years. The Deep-KOA achieved an overall AUROC, sensitivity, specificity, and precision of 0.97, 0.89, 0.93, and 0.80 respectively. The discriminative analysis of Deep-KOA showed important features from several diseases such as disorders of the eye and adnexa, acute respiratory infection, other metabolic and immunity disorders, and diseases of the musculoskeletal and connective tissue. Age and sex were not found as the most discriminative features, with AUROC of 0.9593 (−0.76% loss) and 0.9644 (−0.25% loss) respectively. Whereas medications including antacid, cough suppressant, and expectorants were identified as discriminative features. CONCLUSION: Deep-KOA was developed to predict the risk of KOA within one year earlier, which may provide clues for clinical decision support systems to target patients with high risk of KOA to get precision prevention program. Dove 2021-09-11 /pmc/articles/PMC8445097/ /pubmed/34539180 http://dx.doi.org/10.2147/JMDH.S325179 Text en © 2021 Ningrum et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Ningrum, Dina Nur Anggraini
Kung, Woon-Man
Tzeng, I-Shiang
Yuan, Sheng-Po
Wu, Chieh-Chen
Huang, Chu-Ya
Muhtar, Muhammad Solihuddin
Nguyen, Phung-Anh
Li, Jack Yu-Chuan
Wang, Yao-Chin
A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record
title A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record
title_full A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record
title_fullStr A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record
title_full_unstemmed A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record
title_short A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record
title_sort deep learning model to predict knee osteoarthritis based on nonimage longitudinal medical record
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8445097/
https://www.ncbi.nlm.nih.gov/pubmed/34539180
http://dx.doi.org/10.2147/JMDH.S325179
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