CUILESS2016: a clinical corpus applying compositional normalization of text mentions

Abstract

BackgroundTraditionally text mention normalization corpora have normalized concepts to single ontology identifiers (“pre-coordinated concepts”). Less frequently, normalization corpora have used concepts with multiple identifiers (“post-coordinated concepts”) but the additional identifiers have been restricted to a defined set of relationships to the core concept. This approach limits the ability of the normalization process to express semantic meaning. We generated a freely available corpus using post-coordinated concepts without a defined set of relationships that we term “compositional concepts” to evaluate their use in clinical text.MethodsWe annotated 5397 disorder mentions from the ShARe corpus to SNOMED CT that were previously normalized as “CUI-less” in the “SemEval-2015 Task 14” shared task because they lacked a pre-coordinated mapping. Unlike the previous normalization method, we do not restrict concept mappings to a particular set of the Unified Medical Language System (UMLS) semantic types and allow normalization to occur to multiple UMLS Concept Unique Identifiers (CUIs). We computed annotator agreement and assessed semantic coverage with this method.ResultsWe generated the largest clinical text normalization corpus to date with mappings to multiple identifiers and made it freely available. All but 8 of the 5397 disorder mentions were normalized using this methodology. Annotator agreement ranged from 52.4% using the strictest metric (exact matching) to 78.2% using a hierarchical agreement that measures the overlap of shared ancestral nodes.ConclusionOur results provide evidence that compositional concepts can increase semantic coverage in clinical text. To our knowledge we provide the first freely available corpus of compositional concept annotation in clinical text.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-017-0173-6) contains supplementary material, which is available to authorized users.

SemClinBr - a multi-institutional and multi-specialty semantically annotated corpus for Portuguese clinical NLP tasks

“…To cover temporal reasoning tasks [ 60 ], our annotation schema will be expanded to create a subset of this corpus with temporal annotation. The availability of the corpus to the scientific community will allow not only our research group, but other researchers to complement and adapt the SemClinBr annotations according to their needs, without having to start an annotation process from scratch, as Osborne et al (2018) [ 61 ] did when normalizing the ShARe corpus, or Wagholikar et al who used pooling techniques to reuse corpora across institutions [ 62 ]. Furthermore, the effect of the corpus homogenization process on the performance of these NLP/ML algorithms needs to be determined.…”