CAJ | 학술논문

目的：对肺癌患者住院费用进行分析，了解肺癌住院费用的影响因素，为肺癌费用研究提供一种相对较为完善的评价方法。方法运用Mathlab 7.1模拟神经网络模型，得出各个变量的敏感度评价各因素对住院费用的影响；同时，运用SAS 8.2建立logistic回归模型，将两模型分析结果进行比较。结果两模型结果显示，对住院费用影响前3位的因素分别为：住院天数、药费和出院转归情况。结论 BP神经网络模型与logistic回归都能较好地运用于住院费用影响因素分析，但由于BP神经网络模型拥有适用条件广泛、深入的信息挖掘和影响因素分析充分等优势，因而具有更好的发展和利用前景。
목적：대폐암환자주원비용진행분석，료해폐암주원비용적영향인소，위폐암비용연구제공일충상대교위완선적평개방법。방법운용Mathlab 7.1모의신경망락모형，득출각개변량적민감도평개각인소대주원비용적영향；동시，운용SAS 8.2건립logistic회귀모형，장량모형분석결과진행비교。결과량모형결과현시，대주원비용영향전3위적인소분별위：주원천수、약비화출원전귀정황。결론 BP신경망락모형여logistic회귀도능교호지운용우주원비용영향인소분석，단유우BP신경망락모형옹유괄용조건엄범、심입적신식알굴화영향인소분석충분등우세，인이구유경호적발전화이용전경。
Objective To analyze the costs of hospitalization of patients with lung cancer to understand the influencing factors of lung cancer hospitalization costs, and to provide a relatively consummate evaluation method for lung cancer cost studies. Methods By Mathlab 7. 1, we took age group, occupation and other factors as the input neurons, hospitalization costs the out-put neuron to simulate the analog neural network model, while sensitivity of the derived variables to the neural network model was used to evaluate the factors of hospitalization costs effect. At the same time, we used SAS 8. 2 to build the logistic regression model, and made a comparison between two model results. Results The two model results showed that the top three factors of the hospital cost impact were number of days of hospitalization, drugs, and discharge outcome of the situation. Conclusion BP neural network model and logistic regression can be well applied to the analysis of influencing factors of hospitalization costs, but because of the BP neural network model has a wider range of conditions of application, more in-depth information mining and full depth analysis of the influencing factors, and thus has a better development and utilization prospects.