A cascade of classifiers for extracting medication information from discharge summaries

被引：11

作者：

Halgrim S.R. ^{[1
]}

Xia F. ^{[1
]}

Solti I. ^{[2
]}

Cadag E. ^{[1
]}

Uzuner ^{[3
]}

机构：

[1] University of Washington, PO Box 543450, Seattle, 98195, WA

[2] Cincinnati Children's Hospital Medical Center, Cincinnati, 45229-3039, OH

[3] University of Albany, SUNY, 135 Western Ave, Albany, 12222, NY

来源：

Journal of Biomedical Semantics | / 2卷 / Suppl 3期

基金：

美国国家卫生研究院;

关键词：

Regular Expression; Discharge Summary; Conditional Random Field; Narrative Text; Field Detection;

D O I：

10.1186/2041-1480-2-S3-S2

中图分类号：

学科分类号：

摘要：

Background: 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. Methods: 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. Results: 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. Conclusions: 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. © 2011 Halgrim et al; licensee BioMed Central Ltd.

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