title: Mapping Phenotypic Information in Heterogeneous Textual Sources to a Domain-Specific Terminological Resource.
abstract: Biomedical literature articles and narrative content from Electronic Health Records (EHRs) both constitute rich sources of disease-phenotype information. Phenotype concepts may be mentioned in text in multiple ways, using phrases with a variety of structures. This variability stems partly from the different backgrounds of the authors, but also from the different writing styles typically used in each text type. Since EHR narrative reports and literature articles contain different but complementary types of valuable information, combining details from each text type can help to uncover new disease-phenotype associations. However, the alternative ways in which the same concept may be mentioned in each source constitutes a barrier to the automatic integration of information. Accordingly, identification of the unique concepts represented by phrases in text can help to bridge the gap between text types. We describe our development of a novel method, PhenoNorm, which integrates a number of different similarity measures to allow automatic linking of phenotype concept mentions to known concepts in the UMLS Metathesaurus, a biomedical terminological resource. PhenoNorm was developed using the PhenoCHF corpus-a collection of literature articles and narratives in EHRs, annotated for phenotypic information relating to congestive heart failure (CHF). We evaluate the performance of PhenoNorm in linking CHF-related phenotype mentions to Metathesaurus concepts, using a newly enriched version of PhenoCHF, in which each phenotype mention has an expert-verified link to a concept in the UMLS Metathesaurus. We show that PhenoNorm outperforms a number of alternative methods applied to the same task. Furthermore, we demonstrate PhenoNorm's wider utility, by evaluating its ability to link mentions of various other types of medically-related information, occurring in texts covering wider subject areas, to concepts in different terminological resources. We show that PhenoNorm can maintain performance levels, and that its accuracy compares favourably to other methods applied to these tasks.
authors.affiliation: National Centre for Text Mining, Manchester Institute of Biotechnology, Manchester University, Manchester, United Kingdom.
authors.forename: Noha
authors.identifier: http://orcid.org/0000-0003-3265-0297
authors.initials: N
authors.lastname: Alnazzawi
authors.affiliation: National Centre for Text Mining, Manchester Institute of Biotechnology, Manchester University, Manchester, United Kingdom.
authors.forename: Paul
authors.initials: P
authors.lastname: Thompson
authors.affiliation: National Centre for Text Mining, Manchester Institute of Biotechnology, Manchester University, Manchester, United Kingdom.
authors.forename: Sophia
authors.initials: S
authors.lastname: Ananiadou
mesh_terms: D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records; D006333:Heart Failure / Q000235:genetics; D006801:Humans; D033262:Narration; D009323:Natural Language Processing; D010641:Phenotype; D011655:Pulmonary Embolism / Q000235:genetics; D012660:Semantics; D017432:Unified Medical Language System; D018875:Vocabulary, Controlled