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.

Predictors for success in smoking cessation have been studied, but a prediction model capable of providing a success rate for each patient attempting to quit smoking is still lacking. The aim of this study is to develop prediction models using machine learning algorithms to predict the outcome of smoking cessation. Data was acquired from patients underwent smoking cessation program at one medical center in Northern Taiwan. A total of 4875 enrollments fulfilled our inclusion criteria. Models with artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LoR), k-nearest neighbor (KNN), classification and regression tree (CART), and naïve Bayes (NB) were trained to predict the final smoking status of the patients in a six-month period. Sensitivity, specificity, accuracy, and area under receiver operating characteristic (ROC) curve (AUC or ROC value) were used to determine the performance of the models. We adopted the ANN model which reached a slightly better performance, with a sensitivity of 0.704, a specificity of 0.567, an accuracy of 0.640, and an ROC value of 0.660 (95% confidence interval (CI): 0.617–0.702) for prediction in smoking cessation outcome. A predictive model for smoking cessation was constructed. The model could aid in providing the predicted success rate for all smokers. It also had the potential to achieve personalized and precision medicine for treatment of smoking cessation.

Background: Improvement in left ventricular ejection fraction (LVEF) aligns with better outcomes in patients with heart failure with reduced EF. Nonetheless, detailed relationship between the extent of LVEF improvement and the risks of adverse cardiorenal events remains unclear. Methods: From 2011 to 2021, a retrospective cohort of 601 patients with heart failure with reduced EF who underwent follow‐up echocardiography within 6 months was enrolled and followed until March 31, 2022. Patients were classified as nonimprovement (follow‐up LVEF ≤40% or 40% with 10% to 40% and ≥20% improvement). Multivariable Cox regression was used to estimate associations between LVEF improvement and the composite and individual risks of all‐cause mortality, major adverse cardiovascular events, and major adverse kidney events. Results: Over a median follow‐up of 2.62 years, 67 deaths, 121 major adverse cardiovascular events, and 100 major adverse kidney events occurred. Compared with the nonimprovement group, only the full‐improvement group was independently associated with lower risks of the composite outcome (adjusted hazard ratio [aHR], 0.36 [95% CI, 0.20–0.63]), major adverse cardiovascular events (aHR, 0.33 [95% CI, 0.18–0.61]), and major adverse kidney events (aHR, 0.39 [95% CI, 0.19–0.82]). Graded associations between LVEF improvement and decreased cardiorenal risks were confirmed in spline analyses and consistent across subgroup and sensitivity analyses. Maintenance of improved LVEF for 1 year was further associated with lower risks for the adverse cardiorenal outcomes. Conclusions: Greater and sustained LVEF improvement is linked to lower risks of cardiorenal events in heart failure with reduced EF, emphasizing the value of incorporating improvement magnitude into risk assessment.

Was not provided for this article.