Research on Hospitalization Expenses of 871 Breast Cancer Patients in Beijing from 2019 to 2021 Based on Decision Tree Classification Method
Objectives This study aims to analyze the hospitalization costs of 871 breast cancer patients in Beijing within 3 years by decision tree classification method,laying a theoretical foundation for management department decision-making.Methods The front pages of medical records of 871 inpatients with breast cancer in Beijing from January 1,2019 to December 31,2021 were collected to analyze the composition of hospitalization expenses in different years.All patients were classified into diagnosis-related categories(DRGs)by the decision tree classification model.Absolute value and relative weight methods were used to measure the hospitalization expenses of all breast cancer patients.Results The total hospitalization expenses for breast cancer patients from 2019 to 2021 from high to low were:3,523,965.98 yuan(2020)＜3,763,289.85 yuan(2019)＜3,924,283.54 yuan(2021).In every year,drug expenses accounted for the largest proportion,and nursing and rehabilitation expenses showed an increasing trend year by year.Multiple linear regression analysis showed that treatment methods,medical payment methods,and outcomes have the greatest impact on hospitalization costs,P＜0.05.After the cross-validation method,the decision tree constructed was verified to obtain 12 DRGs combinations,which had high intra-group homogeneity.The number of cases with excessive expenses in each group was around 10％to 12％.Among the DRGs,group 9 had the highest disease weight(1.92),and group 6 among the DRGs had the lowest weight(0.42).Conclusions Drug cost is the biggest economic burden of breast cancer patients during hospitalization.Meanwhile,different treatment methods,medical payment methods,and transfer conditions are the biggest factors affecting the hospitalization cost of breast cancer patients.The DRGs combination of decision tree has high intra-group homogeneity for inpatients with breast cancer,and it has important guiding significance for rational allocation of medical resources after grouping them by DRGs.
Decision tree classificationBreast cancer patientsHospital expensesDecision-making research
NETL
NSTL
万方数据
