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Ningrum DNA, Kung WM, Tzeng IS, et al. A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record. J Multidiscip Healthc. 2021;14:2477-2485doi: 10.2147/JMDH.S325179.
Ningrum, D. N. A., Kung, W. M., Tzeng, I. S., Yuan, S. P., Wu, C. C., Huang, C. Y., Muhtar, M. S., Nguyen, P. A., Li, J. Y., & Wang, Y. C. (2021). A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record. Journal of multidisciplinary healthcare, 142477-2485. https://doi.org/10.2147/JMDH.S325179
Ningrum, Dina Nur Anggraini, et al. "A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record." Journal of multidisciplinary healthcare vol. 14 (2021): 2477-2485. doi: https://doi.org/10.2147/JMDH.S325179
Ningrum DNA, Kung WM, Tzeng IS, Yuan SP, Wu CC, Huang CY, Muhtar MS, Nguyen PA, Li JY, Wang YC. A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record. J Multidiscip Healthc. 2021 Sep 11;14:2477-2485. doi: 10.2147/JMDH.S325179. eCollection 2021. PMID: 34539180; PMCID: PMC8445097.
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J Multidiscip Healthc. 2021 Sep 11;14:2477-2485. doi: 10.2147/JMDH.S325179. eCollection 2021.

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

Journal of multidisciplinary healthcare

Dina Nur Anggraini Ningrum, Woon-Man Kung, I-Shiang Tzeng, Sheng-Po Yuan, Chieh-Chen Wu, Chu-Ya Huang, Muhammad Solihuddin Muhtar, Phung-Anh Nguyen, Jack Yu-Chuan Li, Yao-Chin Wang

Affiliations

  1. Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
  2. International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan.
  3. Public Health Department, Faculty of Sport Science, Universitas Negeri Semarang, Semarang City, Indonesia.
  4. Department of Exercise and Health Promotion, College of Kinesiology and Health, Chinese Culture University, Taipei, Taiwan.
  5. Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, Taiwan.
  6. Department of Statistics, National Taipei University, Taipei, Taiwan.
  7. Department of Otorhinolaryngology, Shuang-Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.
  8. Taiwan College of Healthcare Executives, Taipei, Taiwan.
  9. Department of Healthcare Information and Management, Ming Chuan University, Taoyuan, Taiwan.
  10. Department of Dermatology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
  11. TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei, Taiwan.
  12. Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei, Taiwan.
  13. Department of Emergency Medicine, Min-Sheng General Hospital, Taoyuan, Taiwan.

PMID: 34539180 PMCID: PMC8445097 DOI: 10.2147/JMDH.S325179

Abstract

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.

© 2021 Ningrum et al.

Keywords: artificial intelligence; clinical decision support system; medical informatics application; precision medicine

Conflict of interest statement

The authors report no conflicts of interest in this work.

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