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COLING
2010

Recognizing Medication related Entities in Hospital Discharge Summaries using Support Vector Machine

8 years 6 months ago
Recognizing Medication related Entities in Hospital Discharge Summaries using Support Vector Machine
Due to the lack of annotated data sets, there are few studies on machine learning based approaches to extract named entities (NEs) in clinical text. The 2009 i2b2 NLP challenge is a task to extract six types of medication related NEs, including medication names, dosage, mode, frequency, duration, and reason from hospital discharge summaries. Several machine learning based systems have been developed and showed good performance in the challenge. Those systems often involve two steps: 1) recognition of medication related entities; and 2) determination of the relation between a medication name and its modifiers (e.g., dosage). A few machine learning algorithms including Conditional Random Field (CRF) and Maximum Entropy have been applied to the Named Entity Recognition (NER) task at the first step. In this study, we developed a Support Vector Machine (SVM) based method to recognize medication related entities. In addition, we systematically investigated various types of features for NER ...
Son Doan, Hua Xu
Added 13 May 2011
Updated 13 May 2011
Type Journal
Year 2010
Where COLING
Authors Son Doan, Hua Xu
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