Accurate Identification of Human Phosphorylated Proteins by Ensembling Supervised Kernel Self-organizing Maps

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

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.