根據衛生福利部的資料顯示重大傷病支出約佔健保的三成，其中癌症支出為最多。降低重大傷病患者的心理負擔以及減少健保支出是燃眉之急。多數研究沒有探討癌症跟其他重大傷病之間的關係。而想要做好醫學研究，最重要的是假說。有好的假說才能設計實驗並加以驗證。因此，本研究嘗試找出癌症跟其他重大傷病之間的關係，並以此作為臨床醫學研究之假說。以其幫助臨床醫護人員能快速找到研究假說，也找出癌症跟其他重大傷病的臨床相關性，藉此提醒醫護人員與癌症患者可能再罹患重大傷病之危險，並降低健保支出。
本研究設計從台灣健保資料庫擷取資料，利用關聯性法則方法，找出癌症跟其他重大傷病之間的可能相關性，加上專家判讀，把這些相關性當作臨床研究假說，最後設計臨床實驗來驗證這些相關性確實有其臨床醫學之意義。
在實驗的部份分為兩階段。第一階段，利用Apriori演算法找出癌症跟其他重大傷病之相關性。而在第二階段的實驗，把這些相關性當作醫學研究假說，利用流行病學方法設計研究實驗，來加以驗證這些相關性是真的有臨床意義。實驗結果顯示有腎臟癌患者之後較容易罹患洗腎(Log-rank P < .0001)。
本研究利用關聯性法則，幫忙臨床醫護人員可以快速正確的得到研究假說，並且得到腎臟癌患者較容易罹患洗腎和肺癌患者較容易合併呼吸衰竭。希望可以提醒醫護人員以及癌症患者要注意可能再次罹患重大傷病的危險性，並期望能減少健保支出。

Taiwan Ministry of Health and Welfare data shows that catastrophic illness care accounts for about thirty percent of the total healthcare expenditure, wherein the cost of cancer care is the highest. Reducing the psychological burden of patients with catastrophic illnesses and the cost of national health insurance is pressing. Most studies do not explore the relationship between cancer and other catastrophic illnesses. Moreover, the most important part of medical research is the hypothesis. If we have a good hypothesis, we can design experiments and verify it. Therefore, this study attempts to identify the relationship between cancer and catastrophic illnesses. Then, we use those relationships as hypotheses in clinical medicine research. We hope this method can help physicians quickly and correctly find research hypotheses. We also want to identify the association between cancer and other catastrophic illnesses, to remind healthcare workers and cancer patients that cancer patients may suffer from other catastrophic illnesses, and to reduce national health insurance expenditures.
This study was designed to capture data from the Taiwan National Health Insurance Database, using an association-rule method to identify the associations between cancer and other catastrophic illnesses, while consulting with a medical expert. We used these relationships as hypotheses in clinical medicine research, and finally verified the associations by cohort study.
This experiment was divided into two parts. In the first part, we used the Apriori algorithm to find associations between cancer and other catastrophic illnesses. In the second part, we used these associations as medical-research hypotheses and designed cohort studies to verify them. The result showed that patients with renal cell cancer are more likely to suffer from dialysis than patients without renal cell cancer (Log-rank P < .001).
In this study, we proved that the association-rules method could help clinical physicians quickly and correctly obtain clinical-medicine hypotheses. Those patients with renal cell cancer and lung cancer are more likely to suffer from dialysis and long-term ventilator dependent respiratory failure. We reminded healthcare workers and cancer patients that cancer patients can easily suffer from other, subsequent catastrophic illnesses.

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