Efficient Prediction of Missed Clinical Appointment Using Machine Learning

Retracted: Efficient Prediction of Missed Clinical Appointment Using Machine Learning

[This retracts the article DOI: 10.1155/2021/2376391.].

Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset

OBJECTIVE

Hyperuricemia has become the second most common metabolic disease in China after diabetes, and the disease burden is not optimistic.

METHODS

We used the method of retrospective cohort studies, a baseline survey completed from January to September 2017, and a follow-up survey completed from March to September 2019. A group of 2992 steelworkers was used as the study population. Three models of Logistic regression, CNN, and XG Boost were established to predict HUA incidence in steelworkers, respectively. The predictive effects of the three models were evaluated in terms of discrimination, calibration, and clinical applicability.

RESULTS

The training set results show that the accuracy of the Logistic regression, CNN, and XG Boost models was 84.4, 86.8, and 86.6, sensitivity was 68.4, 72.3, and 81.5, specificity was 82.0, 85.7, and 86.8, the area under the ROC curve was 0.734, 0.724, and 0.806, and Brier score was 0.121, 0.194, and 0.095, respectively. The XG Boost model effect evaluation index was better than the other two models, and similar results were obtained in the validation set. In terms of clinical applicability, the XG Boost model had higher clinical applicability than the Logistic regression and CNN models.

CONCLUSION

The prediction effect of the XG Boost model was better than the CNN and Logistic regression models and was suitable for the prediction of HUA onset risk in steelworkers.

No-Show in Medical Appointments with Machine Learning Techniques: A Systematic Literature Review

No-show appointments in healthcare is a problem faced by medical centers around the world, and understanding the factors associated with no-show behavior is essential. In recent decades, artificial intelligence has taken place in the medical field and machine learning algorithms can now work as an efficient tool to understand the patients’ behavior and to achieve better medical appointment allocation in scheduling systems. In this work, we provide a systematic literature review (SLR) of machine learning techniques applied to no-show appointments aiming at establishing the current state-of-the-art. Based on an SLR following the PRISMA procedure, 24 articles were found and analyzed, in which the characteristics of the database, algorithms and performance metrics of each study were synthesized. Results regarding which factors have a higher impact on missed appointment rates were analyzed too. The results indicate that the most appropriate algorithms for building the models are decision tree algorithms. Furthermore, the most significant determinants of no-show were related to the patient’s age, whether the patient missed a previous appointment, and the distance between the appointment and the patient’s scheduling.