Chiang Yi-Cheng, Hsieh Yin-Chia, Lu Long-Chuan, Ou Shu-Yi
Department of Information Management, National Chung-Cheng University, Chia-Yi 621301, Taiwan.
Taichung Tzu-Chi Hospital, The Buddhist Tzu Chi Medical Foundation, Taichung 427213, Taiwan.
Healthcare (Basel). 2023 May 30;11(11):1598. doi: 10.3390/healthcare11111598.
Due to the increasing cost of health insurance, for decades, many countries have endeavored to constrain the cost of insurance by utilizing a DRG payment system. In most cases, under the DRG payment system, hospitals cannot exactly know which DRG code inpatients are until they are discharged. This paper focuses on the prediction of what DRG code appendectomy patients will be classified with when they are admitted to hospital. We utilize two models (or classifiers) constructed using the C4.5 algorithm and back-propagation neural network (BPN). We conducted experiments with the data collected from two hospitals. The results show that the accuracies of these two classification models can be up to 97.84% and 98.70%, respectively. According to the predicted DRG code, hospitals can effectively arrange medical resources with certainty, then, in turn, improve the quality of the medical care patients receive.
由于医疗保险费用不断上涨，几十年来，许多国家一直致力于通过采用疾病诊断相关分组（DRG）支付系统来控制保险成本。在大多数情况下，在DRG支付系统下，医院直到患者出院才能确切知道住院患者属于哪个DRG编码。本文重点关注阑尾切除术患者入院时将被归类为何种DRG编码的预测。我们使用基于C4.5算法和反向传播神经网络（BPN）构建的两种模型（或分类器）。我们使用从两家医院收集的数据进行了实验。结果表明，这两种分类模型的准确率分别可达97.84%和98.70%。根据预测的DRG编码，医院可以有效地确定安排医疗资源，进而提高患者接受的医疗护理质量。
