背景:肺结核(PTB)是一种普遍的慢性疾病,与患者的重大经济负担有关。利用机器学习对住院费用进行预测,可以有效配置医疗资源,合理优化费用结构,从而更好地控制患者的住院费用。
方法:本研究分析了喀什某肺科医院信息系统(2020-2022年)的数据,涉及9570名符合条件的PTB患者。采用SPSS26.0进行多元回归分析,而Python3.7用于随机森林回归(RFR)和MLP。训练集包括2020年和2021年的数据,而测试集包括2022年的数据。该模型预测了与PTB患者相关的七种不同成本,包括诊断费用,医疗服务费用,材料成本,治疗费用,药费,其他费用,和住院总费用。使用R平方(R2)评估模型的预测性能,均方根误差(RMSE),和平均绝对误差(MAE)指标。
结果:在纳入研究的9570名PTB患者中,住院总费用的中位数和四分位数分别为13,150.45元(9891.34,19,648.48元).九个因素,包括年龄,婚姻状况,入院条件,住院时间,初始治疗,其他疾病的存在,转让,耐药性,和招生部门,显著影响PTB患者的住院费用。总的来说,MLP在大多数成本预测中表现出卓越的性能,表现优于RFR和多元回归;RFR的性能介于MLP和多元回归之间;多元回归的预测性能最低,但它显示了其他成本的最佳结果。
结论:MLP可以有效利用患者信息,准确预测各种住院费用,通过调整成本较高的住院项目和平衡不同的费用类别,实现住院费用的合理化结构。这种预测模型的见解也与其他医疗条件的研究相关。
BACKGROUND: Pulmonary tuberculosis (PTB) is a prevalent chronic disease associated with a significant economic burden on patients. Using machine learning to predict hospitalization costs can allocate medical resources effectively and optimize the cost structure rationally, so as to control the hospitalization costs of patients better.
METHODS: This research analyzed data (2020-2022) from a Kashgar pulmonary hospital\'s information system, involving 9570 eligible PTB patients. SPSS 26.0 was used for multiple regression analysis, while Python 3.7 was used for random forest regression (RFR) and MLP. The training set included data from 2020 and 2021, while the test set included data from 2022. The models predicted seven various costs related to PTB patients, including diagnostic cost, medical service cost, material cost, treatment cost, drug cost, other cost, and total hospitalization cost. The model\'s predictive performance was evaluated using R-square (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) metrics.
RESULTS: Among the 9570 PTB patients included in the study, the median and quartile of total hospitalization cost were 13,150.45 (9891.34, 19,648.48) yuan. Nine factors, including age, marital status, admission condition, length of hospital stay, initial treatment, presence of other diseases, transfer, drug resistance, and admission department, significantly influenced hospitalization costs for PTB patients. Overall, MLP demonstrated superior performance in most cost predictions, outperforming RFR and multiple regression; The performance of RFR is between MLP and multiple regression; The predictive performance of multiple regression is the lowest, but it shows the best results for Other costs.
CONCLUSIONS: The MLP can effectively leverage patient information and accurately predict various hospitalization costs, achieving a rationalized structure of hospitalization costs by adjusting higher-cost inpatient items and balancing different cost categories. The insights of this predictive model also hold relevance for research in other medical conditions.