Mining and understanding patients’ disease-development pattern is a major healthcare need. A huge number of research studies have focused on medical resource allocation, survivability prediction, risk management of diagnosis, etc. In this article, we are specifically interested in discovering risk factors for patients with high probability of developing cancers. We propose a systematic and data-driven algorithm and build around the idea of association rule mining. More precisely, the rule-mining method is firstly applied on the target dataset to unpack the underlying relationship of cancer-risk factors, via generating a set of candidate rules. Later, this set is represented as a rule graph, where informative rules are identified and selected with the aim of enhancing the result interpretability. Compared to hundreds of rules generated from the standard rule-mining approach, the proposed algorithm benefits from a concise rule subset, without losing the information from the original rule set. The proposed algorithm is then evaluated using one of the largest cancer data resources. We found that our method outperforms existing approaches in terms of identifying informative rules and requires affordable computational time. Additionally, relevant information from the selected rules can also be used to inform health providers and authorities for cancer-risk management.

中文翻译：

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使用图规则挖掘进行癌症生存能力分析的危险因素发现

挖掘和了解患者的疾病发展模式是主要的医疗保健需求。大量研究集中在医疗资源分配，生存能力预测，诊断风险管理等方面。在本文中，我们特别感兴趣的是发现极有可能罹患癌症的患者的风险因素。我们提出了一种系统的，数据驱动的算法，并以关联规则挖掘的思想为基础。更准确地说，规则挖掘方法首先通过生成一组候选规则而应用于目标数据集，以解开癌症风险因素的潜在关系。后来，此集合被表示为规则图，其中识别和选择信息性规则以增强结果的可解释性。与标准规则挖掘方法生成的数百条规则相比，该算法受益于简洁的规则子集，而不会丢失原始规则集中的信息。然后使用最大的癌症数据资源之一评估提出的算法。我们发现，在识别信息规则方面，我们的方法优于现有方法，并且需要可负担的计算时间。另外，来自选定规则的相关信息也可以用于告知健康提供者和当局以进行癌症风险管理。我们发现，在识别信息规则方面，我们的方法优于现有方法，并且需要可负担的计算时间。另外，来自选定规则的相关信息也可以用于告知健康提供者和当局以进行癌症风险管理。我们发现，在识别信息规则方面，我们的方法优于现有方法，并且需要可负担的计算时间。另外，来自选定规则的相关信息也可以用于告知健康提供者和当局以进行癌症风险管理。