| Literature DB >> 21347081 |
Sunghwan Sohn1, Sean P Murphy, James J Masanz, Jean-Pierre A Kocher, Guergana K Savova.
Abstract
The patient's medication history and status changes play essential roles in medical treatment. A notable amount of medication status information typically resides in unstructured clinical narratives that require a sophisticated approach to automated classification. In this paper, we investigated rule-based and machine learning methods of medication status change classification from clinical free text. We also examined the impact of balancing training data in machine learning classification when using the data from skewed class distribution.Entities:
Mesh:
Year: 2010 PMID: 21347081 PMCID: PMC3041444
Source DB: PubMed Journal: AMIA Annu Symp Proc ISSN: 1559-4076