SNOMED CT

AbstractThis paper describes the development of a new Swedish scientific medical corpus. We provide a detailed description of the characteristics of this new collection as well results of an application …...

. Misspellings, abbreviations and acronyms are very popular in clinical notes and can be an obstacle to high quality information extraction and classification. In addition, another important part of narrative …...

AbstractNamed entity recognition of the clinical entities disorders, findings and body structures is needed for information extraction from unstructured text in health records. Clinical notes from a Swedish emergency unit …...

This paper describes our participation on Task 7 of SemEval 2014, which fo- cused on the recognition and disambigua- tion of medical concepts. We used an adapted version of the …...

AbstractClinical letters are infamously impenetrable for the lay patient. This work uses neural text simplification methods to automatically improve the understandability of clinical letters for patients. We take existing neural …...

AbstractWhilst there has been growing progress in Entity Linking (EL) for general language, existing datasets fail to address the complex nature of health terminology in layman’s language. Meanwhile, there is …...

AbstractConcept normalization of clinical texts to standard medical classifications and ontologies is a task with high importance for healthcare and medical research. We attempt to solve this problem through automatic …...

AbstractDespite remarkable advances in the development of language resources over the recent years, there is still a shortage of annotated, publicly available corpora covering (German) medical language. With the initial …...

AbstractWe present an approach for medical text coding with SNOMED CT. Our approach uses publicly available linked open data from terminologies and ontologies as training data for the algorithms. We …...