Calculating Prevalence of Comorbidity and Comorbidity Combinations with Diabetes in Hospital Care in Sweden Using a Health Care Record Database
2011 (English)Conference paper (Refereed)
Access to reliable data from electronic health records is of high importance in several key areas in patient care, biomedical research, and education. However, many of the clinical entities are negated in the patient record text. Detecting what is a negation and what is not is therefore a key to high quality text mining. In this study we used the NegEx system adapted for Swedish to investigate negated clinical entities. We applied the system to a subset of free-text entries under a heading containing the word assessment from the Stockholm EPR corpus, containing in total 23,171,559 tokens. Specifically, the explored entities were the SNOMED CT terms having the semantic categories finding or disorder. The study showed that the proportion of negated clinical entities was around 9%. The results thus support that negations are abundant in clinical text and hence negation detection is vital for high quality text mining in the medical domain.
Place, publisher, year, edition, pages
Øystein Nytrø, Laura Slaughter, Hans Moen , 2011.
Negation detection, Clinical text, Electronic patient records, SNOMED CT, Swedish
Research subject Computer and Systems Sciences
IdentifiersURN: urn:nbn:se:su:diva-63487OAI: oai:DiVA.org:su-63487DiVA: diva2:450258