Internal medicine inpatients’ prevalence of misdiagnosed severe osteoporosis

Casella, María Cristina Bazán de; Becciolini, Andrea; Donato, Eleonora Di; Basaglia, Manuela; Zardo, Marianna; Lucchini, Giovanna; Riva, M.; Ariani, Alarico; Magalini, Francesca

doi:10.1007/s00198-021-05976-6

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…Currently, bone mineral density (BMD) examination is still not included as a generalized screening tool during regular health checkup in Taiwan. Moreover, the presence of osteoporosis is often overlooked in clinical practice [10]. To identify patients at risk and to increase the awareness of physicians for asymptomatic osteoporosis, understanding the risk factors and proper interpretation is crucial.…”

Section: Introductionmentioning

confidence: 99%

Development of Machine Learning Models for Prediction of Osteoporosis from Clinical Health Examination Data

Wenyu

Lai

Tsou

et al. 2021

IJERPH

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Osteoporosis is treatable but often overlooked in clinical practice. We aimed to construct prediction models with machine learning algorithms to serve as screening tools for osteoporosis in adults over fifty years old. Additionally, we also compared the performance of newly developed models with traditional prediction models. Data were acquired from community-dwelling participants enrolled in health checkup programs at a medical center in Taiwan. A total of 3053 men and 2929 women were included. Models were constructed for men and women separately with artificial neural network (ANN), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and logistic regression (LoR) to predict the presence of osteoporosis. Area under receiver operating characteristic curve (AUROC) was used to compare the performance of the models. We achieved AUROC of 0.837, 0.840, 0.843, 0.821, 0.827 in men, and 0.781, 0.807, 0.811, 0.767, 0.772 in women, for ANN, SVM, RF, KNN, and LoR models, respectively. The ANN, SVM, RF, and LoR models in men, and the ANN, SVM, and RF models in women performed significantly better than the traditional Osteoporosis Self-Assessment Tool for Asians (OSTA) model. We have demonstrated that machine learning algorithms improve the performance of screening for osteoporosis. By incorporating the models in clinical practice, patients could potentially benefit from earlier diagnosis and treatment of osteoporosis.

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Section: Introductionmentioning

confidence: 99%