Cancer Hospital of China Medical University, Shenyang, China.
Liaoning Cancer Hospital & Institute, Shenyang, China.
BMC Med Inform Decis Mak. 2021 Oct 16;21(1):284. doi: 10.1186/s12911-021-01647-y.
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.
按病种收费是有效推动医保机制改革、合理配置医疗资源、减轻患者负担的重要手段，也是国内外医疗发展的重要方向。单病种成本预测不仅可以发现潜在的影响和驱动因素，还可以预估主动成本，并为医疗资源的管理和合理配置提供依据。针对数据量较少的情况下，基于患者电子病历预测治疗费用的问题，本文提出了一种贝叶斯网络结合回归分析的方法。首先，建立了一套基于文本的医疗记录数据转换方法，在聚类方法中，根据距离采用加权方法进行缺失值插值，从而完成数据预测的准备和处理。然后，针对传统回归模型预测精度低的问题，本文在对患者治疗过程进行贝叶斯网络解释和分类的基础上，建立了结合局部权重回归方法的预测模型。最后，利用医院提供的病历数据对模型进行验证，结果表明该模型具有较高的预测精度。
