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Mining association rules between abnormal health examination results and outpatient medical records

机译：异常健康检查结果与门诊病历之间的关联规则挖掘

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Currently, interpretation of health examination reports relies primarily on the physician's own experience. If health screening data could be integrated with outpatient medical records to uncover correlations between disease and abnormal test results, the physician could benefit from having additional reference resources for medical examination report interpretation and clinic diagnosis. This study used the medical database of a regional hospital in Taiwan to illustrate how association rules can be found between abnormal health examination results and outpatient illnesses. The rules can help to build up a disease-prevention knowledge database that assists healthcare providers in follow-up treatment and prevention. Furthermore, this study proposes a new algorithm, the data cutting and sorting method, or DCSM, in place of the traditional Apriori algorithm. DCSM significantly improves the mining performance of Apriori by reducing the time to scan health examination and outpatient medical records, both of which are databases of immense sizes.
机译：当前，对健康检查报告的解释主要取决于医师自身的经验。如果可以将健康检查数据与门诊病历相结合，以发现疾病和异常检查结果之间的相关性，那么医生可以从拥有更多参考资源的医学检查报告解释和临床诊断中受益。这项研究使用台湾一家地区医院的医学数据库来说明如何在异常健康检查结果与门诊疾病之间找到关联规则。这些规则可以帮助建立疾病预防知识数据库，以帮助医疗保健提供者进行后续治疗和预防。此外，本研究提出了一种新的算法，即数据切割和排序方法，即DCSM，代替了传统的Apriori算法。 DCSM通过减少扫描健康检查和门诊病历的时间（这两者都是庞大的数据库），大大提高了Apriori的采矿性能。

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