Application of Bayesian network and regression method in treatment cost prediction

Abstract

Charging according to disease is an important way to effectively promote the reform of medical insurance mechanism, reasonably allocate medical resources and reduce the burden of patients, and it is also an important direction of medical development at home and abroad. The cost forecast of single disease can not only find the potential influence and driving factors, but also estimate the active cost, and tell the management and reasonable allocation of medical resources. In this paper, a method of Bayesian network combined with regression analysis is proposed to predict the cost of treatment based on the patient's electronic medical record when the amount of data is small. Firstly, a set of text-based medical record data conversion method is established, and in the clustering method, the missing value interpolation is carried out by weighted method according to the distance, which completes the data preparation and processing for the realization of data prediction. Then, aiming at the problem of low prediction accuracy of traditional regression model, this paper establishes a prediction model combined with local weight regression method after Bayesian network interpretation and classification of patients' treatment process. Finally, the model is verified with the medical record data provided by the hospital, and the results show that the model has higher prediction accuracy.

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

“…This metric is often used for creating predictive Bayesian networks in medical tasks [ 25 , 26 ] and results in high performance of the created models. For example, in the paper [ 25 ], BN showed 0.89 AUC-ROC quality in a task of medical classification.…”

“…This result was better than the results of a logistic regression model and naive Bayesian classifier. In task [ 26 ], the Bayesian network and regression method were implicated in treatment cost prediction. This model showed 89.14 accuracy, which is better than the second result (by locally weighted LASSO regression model) by 4%.…”