Email updates

Keep up to date with the latest news and content from BMC Medical Informatics and Decision Making and BioMed Central.

Open Access Highly Accessed Research article

Recognition of medication information from discharge summaries using ensembles of classifiers

Son Doan1*, Nigel Collier1, Hua Xu2, Pham Hoang Duy3 and Tu Minh Phuong3

Author Affiliations

1 National Institute of Informatics, Hitotsubashi, Chiyoda, Tokyo, Japan

2 Department of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN, USA

3 Department of Computer Science, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam

For all author emails, please log on.

BMC Medical Informatics and Decision Making 2012, 12:36  doi:10.1186/1472-6947-12-36

Published: 7 May 2012

Abstract

Background

Extraction of clinical information such as medications or problems from clinical text is an important task of clinical natural language processing (NLP). Rule-based methods are often used in clinical NLP systems because they are easy to adapt and customize. Recently, supervised machine learning methods have proven to be effective in clinical NLP as well. However, combining different classifiers to further improve the performance of clinical entity recognition systems has not been investigated extensively. Combining classifiers into an ensemble classifier presents both challenges and opportunities to improve performance in such NLP tasks.

Methods

We investigated ensemble classifiers that used different voting strategies to combine outputs from three individual classifiers: a rule-based system, a support vector machine (SVM) based system, and a conditional random field (CRF) based system. Three voting methods were proposed and evaluated using the annotated data sets from the 2009 i2b2 NLP challenge: simple majority, local SVM-based voting, and local CRF-based voting.

Results

Evaluation on 268 manually annotated discharge summaries from the i2b2 challenge showed that the local CRF-based voting method achieved the best F-score of 90.84% (94.11% Precision, 87.81% Recall) for 10-fold cross-validation. We then compared our systems with the first-ranked system in the challenge by using the same training and test sets. Our system based on majority voting achieved a better F-score of 89.65% (93.91% Precision, 85.76% Recall) than the previously reported F-score of 89.19% (93.78% Precision, 85.03% Recall) by the first-ranked system in the challenge.

Conclusions

Our experimental results using the 2009 i2b2 challenge datasets showed that ensemble classifiers that combine individual classifiers into a voting system could achieve better performance than a single classifier in recognizing medication information from clinical text. It suggests that simple strategies that can be easily implemented such as majority voting could have the potential to significantly improve clinical entity recognition.