An integrated model for medical expense system optimization during diagnosis process based on artificial intelligence algorithm

Abstract

In the era of artificial intelligence, the healthcare industry is undergoing tremendous innovation and development based on sophisticated AI algorithms. Focusing on diagnosis process and target disease, this study theoretically proposed an integrated model to optimize traditional medical expense system, and ultimately helps medical staff and patients make more reliable decisions. From the new perspective of total expense estimation and detailed expense analysis, the proposed model innovatively consists of two intelligent modules, with theoretical contribution. The two modules are SVM-based module and SOM-based module. According to the rigorous comparative analysis with two classic AI techniques, back propagation neural networks and random forests, it is demonstrated that the SVM-based module achieved better capability of total expense estimation. Meanwhile, by designing a two-stage clustering process, SOM-based module effectively generated decision clusters and corresponding cluster centers were obtained, that clarified the complex relationship between detailed expense and patient information. To achieve practical contribution, the proposed model was applied to the diagnosis process of coronary heart disease. The real data from a hospital in Shanghai was collected, and the validity and accuracy of the proposed model were verified with rigorous experiments. The proposed model innovatively optimized traditional medical expense system, and intelligently generated reliable decision-making information for both total expense and detailed expense. The successful application on the target disease further indicates that this model is a user-friendly tool for medical expense control and therapeutic regimen strategy.

Research on Evaluation Methods for Sustainable Enrollment Plan Configurations in Chinese Universities Based on Bayesian Networks

“…Cubic spline interpolation fits cubic polynomials between adjacent data points, ensuring smoothness as the function passes through all points with continuous first and second derivatives [23]. That is why we chose this method to fill in missing values in our dataset.…”

Prediction of fractional flow reserve with enhanced ant lion optimized support vector machine

“…Huang et al [ 19 ] proposed a multi-scale feature fusion depth residual division network based on attention mechanism, and realizes the accurate segmentation of CHD blood chambers. Huang et al [ 20 ] developed a comprehensive model for diagnosing coronary heart disease, demonstrating its user-friendly nature as a valuable tool for reducing medical costs and devising optimal treatment plans. Wang et al [ 21 ] used a deep learning method to diagnose coronary heart disease and effectively verify the comprehensive performance of the method by comparison with traditional methods.…”