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 in clinical text. Evaluation on 268 manually annotated discharge summaries from i2b2 challenge showed that the SVM-based NER system achieved the best F-score of 90.05% (93.20% Precision, 87.12% Recall), when semantic features generated from a rule-based system were included.