以健保資料庫建構頭頸癌併發吸入性肺炎高風險病患之預測模式

李彥賢; 賴家玄; 蔡佳玲

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P341 - 367

基础与应用科学 > 资讯科学
社会科学 > 管理学

DOI： 10.6382/JIM

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以健保资料库建构头颈癌并发吸入性肺炎高风险病患之预测模式

A Prediction Model for Head and Neck Cancer Patient Complicated with Aspiration Pneumonia

李彦贤(Yen-Hsien Lee) ；赖家玄(Chia-Hsuan Lai) ；蔡佳玲(Jia-Ling Cai)

资讯管理学报； 24 卷 3 期 (2017 / 07 / 31) ， P341 - 367

繁体中文

头颈癌；吸入性肺炎；国民健康保险资料库；倾向分数配对；整体学习演算法； head and neck cancer ； aspiration pneumonia ； National Health Insurance Research Database ； propensity score matching ； ensemble learning

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摘要 │ 参考文献 (48) │ 国际计量

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Purpose-The treatment-related adverse effects of head and neck cancer and/or the anatomic location of tumors are likely to cause swallowing problems that might lead to the complications such as choking, malnutrition, and aspiration pneumonia. Prior research indicated the 12-month death rate of head and neck cancer patient with the complication of aspiration pneumonia is nearly 10%. The factors that cause the complication of aspiration pneumonia have been observed in prior studies but inconclusive. This study aims to discover Taiwan’s National Health Insurance Research Database, the most comprehensive records of medical insurance claim in Taiwan, to construct a prediction model for the head and neck cancer patients who are at risk of aspiration pneumonia. Design/methodology/approach - We reviewed the literature to identify a collective set of thirteen factors, which are relevant to the head and neck cancer patients with the complication of aspiration pneumonia and whose data values are available in Taiwan’s National Health Insurance Research Database, and adopted them as independent variables. We used propensity score matching to create training dataset and implemented bagging-based and boosting-based ensemble learning methods with different learning algorithms to construct prediction models. Findings－The results suggested that the five investigated approaches were effective in predicting the head and neck cancer patients at risk of aspiration pneumonia. The prediction performances achieved by boosting-based ensemble learning methods were better than bagging-based ones. Overall, the proposed approach can be promising to the construction of prediction model for the head and neck cancer patients with higher risk of aspiration pneumonia using Taiwan’s National Health Insurance Research Database. Research limitations/implications－This study applies ensemble learning to construct the prediction model for predicting the head and neck cancer patients at risk of aspiration pneumonia. The evaluation results reveal the effectiveness and the practicability of the proposed method, which builds the prediction model based on health insurance database. This study has contributed to the research area of health data mining. Nevertheless, the independent variables used to construct the prediction model are limited to the records of medical insurance claim. Future research is suggested to incorporate other data sets, such as medical records into the construction of prediction models. Practical implications-The proposed method can be developed into a decision support system to support physicians in assessing the head and neck cancer patients who are at risk of aspiration pneumonia. Such patients can be well educated in advance to prevent the occurrence of aspiration pneumonia. The development of such system is feasible because the records of the medical insurance claim required for constructing the prediction model are ready available. Originality/value-This study investigated the factors that may cause the complication of aspiration pneumonia, thereby constructing a prediction model based on the health insurance database to predict the head and neck cancer patients who are at risk. We developed a method for database preprocessing, training dataset creation, and prediction model construction. The evaluation results suggested practicability and effectiveness of the proposed method.

参考文献 ( 48 ) 〈TOP〉

陈锦华（2014），『倾向分数（propensity score）在估计风险比之使用方法』，台北医学大学生物统计研究中心 eNews，第二卷。
Adnet, F.,Baud, F.(1996).Relation between Glasgow Coma Scale and aspiration pneumonia.Lancet,348(9020),123-124.
Bauer, E.,Kohavi, R.(1999).An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants.Machine Learning,36(1),105-139.
Baum, G.L.,Crapo, J.D.,Celli, B.R.,Karlinsky, J.B.(1998).Textbook of Pulmonary Diseases.Philadelphia:Lippincott Williams & Wilkins.
Beasley, R.P.,Lin, C.C.,Hwang, L.Y.,Chien, C.S.(1981).Hepatocellular carcinoma and hepatitis B virus: a prospective study of 22 707 men in Taiwan.Lancet,318(8256),1129-1133.

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