Halgrim Scott Russell, Xia Fei, Solti Imre, Cadag Eithon, Uzuner Ozlem
University of Washington, PO Box 543450, Seattle, WA 98195, USA.
J Biomed Semantics. 2011;2 Suppl 3(Suppl 3):S2. doi: 10.1186/2041-1480-2-S3-S2. Epub 2011 Jul 14.
Extracting medication information from clinical records has many potential applications, and recently published research, systems, and competitions reflect an interest therein. Much of the early extraction work involved rules and lexicons, but more recently machine learning has been applied to the task.
We present a hybrid system consisting of two parts. The first part, field detection, uses a cascade of statistical classifiers to identify medication-related named entities. The second part uses simple heuristics to link those entities into medication events.
The system achieved performance that is comparable to other approaches to the same task. This performance is further improved by adding features that reference external medication name lists.
This study demonstrates that our hybrid approach outperforms purely statistical or rule-based systems. The study also shows that a cascade of classifiers works better than a single classifier in extracting medication information. The system is available as is upon request from the first author.
从临床记录中提取用药信息有许多潜在应用，最近发表的研究、系统和竞赛都反映了对此的兴趣。早期的提取工作大多涉及规则和词典，但最近机器学习已应用于该任务。
我们提出了一个由两部分组成的混合系统。第一部分，字段检测，使用级联统计分类器来识别与用药相关的命名实体。第二部分使用简单启发式方法将这些实体链接成用药事件。
该系统实现了与执行相同任务的其他方法相当的性能。通过添加引用外部用药名称列表的特征，性能得到进一步提高。
本研究表明，我们的混合方法优于纯统计或基于规则的系统。该研究还表明，在提取用药信息时，级联分类器比单个分类器效果更好。如有需要，可向第一作者索取该系统。
