TY - JOUR
T1 - A deep learning model to predict knee osteoarthritis based on nonimage longitudinal medical record
AU - Ningrum, Dina Nur Anggraini
AU - Kung, Woon Man
AU - Tzeng, I. Shiang
AU - Yuan, Sheng Po
AU - Wu, Chieh Chen
AU - Huang, Chu Ya
AU - Muhtar, Muhammad Solihuddin
AU - Nguyen, Phung Anh
AU - Li, Yu-Chuan
AU - Wang, Yao Chin
N1 - Funding Information:
The first author thanks the Directorate General of Resources for Science, Technology and Higher Education, at the Ministry of Education and Culture, Republic Indonesia for the sponsorship of her doctoral study. The authors are also grateful to Md. Mohaimenul Islam, PhD (International Center for Health Information Technology, Taipei Medical University) who provided advices and assisted on the process of drafting the manuscript. They were not compensated for their contributions. Jack Yu-Chuan Li and Yao-Chin Wang contributed equally as co-corresponding authors for this study.
Publisher Copyright:
© 2021 Ningrum et al.
PY - 2021
Y1 - 2021
N2 - 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 respec-tively. 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.
AB - 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 respec-tively. 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.
KW - Artificial intelligence
KW - Clinical decision support system
KW - Medical informatics application
KW - Precision medicine
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U2 - 10.2147/JMDH.S325179
DO - 10.2147/JMDH.S325179
M3 - Article
AN - SCOPUS:85115267766
SN - 1178-2390
VL - 14
SP - 2477
EP - 2485
JO - Journal of Multidisciplinary Healthcare
JF - Journal of Multidisciplinary Healthcare
ER -