Objective

This work aims at deriving interesting clinical events using association rule mining based on a user-annotated order of clinical features.

Materials and methods

A user specifies a partial temporal order of features by indexing features of interest, with repeated and bundled indexes allowed as needed. An association mining algorithm plugin was designed to generate rules that adhere to the user-specified temporal order. The plugin uses temporal and sequence constraints to reduce rule permutations early in the rule generation process. The method was evaluated with a large medical claims dataset to generate clinical events.

Results

Using the plug-in algorithm, the database is scanned to calculate the support of item sequences whose sequential order conforms with the user annotated feature order. In our experiments with 20,000 medical claim data records, our method generated rules in a significantly less time than the standalone Apriori algorithm. Our approach generates dendrograms to organize the rules into meaningful hierarchies and provides a graphical interface to navigate the rules and unfold interesting clinical events.

Discussion

Since many associations in healthcare are of sequential nature, some of the derived rules may describe interesting clinical flows or events, while others may be contextually irrelevant. Our method exploits user-specified sequence constraints to eliminate irrelevant rules and reduce rule permutations, speeding up rule mining.

Conclusion

This work can be the foundation for future association rule mining studies to extract sequential events based on interestingness. The work can support clinical education where the instructor defines feature sequence constraints, and students unfold and examine extracted sequential rules.

中文翻译：

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带注释的关联挖掘方法，用于提取和可视化有趣的临床事件

目的

这项工作旨在基于用户注释的临床特征顺序，使用关联规则挖掘来得出有趣的临床事件。

材料和方法

用户通过索引感兴趣的特征来指定特征的部分时间顺序，并根据需要允许重复和捆绑的索引。关联挖掘算法插件被设计为生成符合用户指定时间顺序的规则。该插件使用时间和顺序约束来在规则生成过程的早期减少规则排列。该方法已通过大型医疗索赔数据集进行了评估，以产生临床事件。

结果

使用插件算法，对数据库进行扫描以计算其顺序顺序与用户注释的特征顺序一致的项序列的支持。在具有20,000个医疗索赔数据记录的实验中，与独立的Apriori算法相比，我们的方法在更少的时间内生成了规则。我们的方法生成树状图，将规则组织为有意义的层次结构，并提供图形界面来导航规则并展开有趣的临床事件。

讨论区

由于医疗保健中的许多关联具有顺序性质，因此某些派生规则可能描述了有趣的临床流程或事件，而其他规则可能与上下文无关。我们的方法利用用户指定的序列约束来消除不相关的规则并减少规则排列，从而加快规则挖掘的速度。

结论

这项工作可以为将来的关联规则挖掘研究基于兴趣提取顺序事件的基础。这项工作可以为临床教育提供支持，其中教师定义了特征序列约束，学生可以展开并检查提取的序列规则。