Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi)

Abstract

Narrative information in Electronic Health Records (EHRs) and literature articles contains a wealth of clinical information about treatment, diagnosis, medication and family history. This often includes detailed phenotype information for specific diseases, which in turn can help to identify risk factors and thus determine the susceptibility of different patients. Such information can help to improve healthcare applications, including Clinical Decision Support Systems (CDS). Clinical text mining (TM) tools can provide efficient automated means to extract and integrate vital information hidden within the vast volumes of available text. Development or adaptation of TM tools is reliant on the availability of annotated training corpora, although few such corpora exist for the clinical domain. In response, we have created a new annotated corpus (PhenoCHF), focussing on the identification of phenotype information for a specific clinical sub-domain, i.e., congestive heart failure (CHF). The corpus is unique in this domain, in its integration of information from both EHRs (300 discharge summaries) and literature articles (5 full-text papers). The annotation scheme, whose design was guided by a domain expert, includes both entities and relations pertinent to CHF. Two further domain experts performed the annotation, resulting in high quality annotation, with agreement rates up to 0.92 F-Score.