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Public health and its related facilities are crucial for thriving cities and societies. The optimum utilization of health resources saves money and time, but above all, it saves precious lives. It has become even more evident in the present as the pandemic has overstretched the existing medical resources. Specific to patient appointment scheduling,...
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Citations
... The convolutional neural network (CNN) is a feedforward neural network with a deep structure that is good at mining local features of data and extracting global training features and classification and has some advantages that traditional techniques do not have [8]. XG Boost, known as eXtreme gradient boosting, achieves classification by iterative computation of classifiers, and the addition of its regular term ensures the model's robustness and reduces the time to process features because it was good at handling missing data [9]. We established the above three HUA morbidity risk prediction models based on the medical examination data information of more than two thousand steelworkers and compared their prediction effects, aiming to select the optimal model and provide a theoretical basis for the health management of this special occupational group. ...
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.
... The origin of the data was concentrated in nine different countries. Only the work by Qureshi et al. [10] did not describe the origin of the data used. Followed by the United States, Brazil was the country of origin of eight studies, seven of which used the same dataset. ...
... Symbols "x" and "o" represent tested and best performance algorithms, respectively. [24] x x [26] x x [10] x ...
... Four works [10,18,20,26] only presented the exploratory analysis of the data and the steps for the model building. However, they did not implement or discuss software and/or process management solutions that could be developed based on the study. ...
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.
No-show appointments in healthcare is a problem faced by medical centers around the world, and understand the factors associated with the no-show behavior is essential. In the last decades, artificial intelligence took place in the medical field and machine learning algorithms can work as a 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 a SLR following the Kitchenham methodology, 24 articles were found and analyzed, in which the characteristics of the database, algorithms and performance metrics of each studies 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 patients age, whether the patient missed a previous appointment, and the distance between the appointment and the patients scheduling.
