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.
Highlights
Osteoporosis is a global health problem for ageing populations
In the research area of image analysis, machine learning was applied with magnetic resonance imaging (MRI) and computerized tomography (CT) data and the results showed the capability of screening and predicting osteoporotic fractures[31,32]
The dataset of 13,562 osteoporosis treatment profiles taken from January 2011 to December 2019 were used for this study
Summary
Osteoporosis is a global health problem for ageing populations. The goals of osteoporosis treatment are to improve bone mineral density (BMD) and prevent fractures. Focusing on BMD and fracture risk as treatment goals, a goaldirected therapy was proposed to individually select an initial treatment on its probability of reaching expected BMD By following this treatment approach, physicians are required to regularly assess the risk of fracture during treatment using Fracture Risk Assessment Tool (FRAX)® score and adjust regimens with patient-related factors[9]. A decrease of less than 3% of lumbar BMD or less than 5% total hip or femoral neck BMD are considered “response” to therapy, while those who have new fractures or a BMD decrease exceeding the aforementioned criteria are considered “inadequate response”[10,11,12] This optimal approach is not always fully implemented depending on the availability of medications[13] and the cost of treatment which varies across countries. Factors that interfere with the treatment outcome, such as BMD before treatment, history of falls, laboratory results, FRAX® score, comorbid conditions, current use of glucocorticoid, secondary osteoporosis, and adhering to treatment[14,16,17]
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