Improving textual medication extraction using combined conditional random fields and rule-based systems.

ABSTRACT:

Objective

In the i2b2 Medication Extraction Challenge, medication names together with details of their administration were to be extracted from medical discharge summaries.

Design

The task of the challenge was decomposed into three pipelined components: named entity identification, context-aware filtering and relation extraction. For named entity identification, first a rule-based (RB) method that was used in our overall fifth place-ranked solution at the challenge was investigated. Second, a conditional random fields (CRF) approach is presented for named entity identification (NEI) developed after the completion of the challenge. The CRF models are trained on the 17 ground truth documents, the output of the rule-based NEI component on all documents, a larger but potentially

SUBMITTER: Tikk D 

PROVIDER: S-EPMC2995683 | biostudies-literature | 2010 Sep-Oct

REPOSITORIES: biostudies-literature