Election of Diagnosis Codes: Words as Responsible Citizens
2011 (English)Conference paper (Refereed)
Providing computer-aided support for the assignment of diagnosis codes has been approached in numerous ways, often by exploiting free-text fields in patient records. Modeling the 'meaning' of diagnosis codes through statistical data on co-occurrences of words and assigned codes - using a method known as Random Indexing - has only recently been explored as an interesting, alternative solution. It involves words in a clinician's notes 'voting' for semantically associated diagnosis codes, the election results yielding a single list of recommendations. This approach is here applied and evaluated on a corpus of over 250,000 coded patient records. The evaluation is performed by comparing the recommended codes generated by the model with those assigned by the clinicians. Applying the tf-idf weighting scheme somewhat improves results for general models (23% recall for exact matches) but has little effect on domain-specific models (32% and 59% recall for exact matches). These results confirm the potential of Random Indexing for diagnosis code assignment support, and merits further attention.
Place, publisher, year, edition, pages
CEUR-WS.org , 2011.
Diagnosis Code Assignment, ICD-10, Random Indexing, Electronic Patient Records
Research subject Computer and Systems Sciences
IdentifiersURN: urn:nbn:se:su:diva-62346OAI: oai:DiVA.org:su-62346DiVA: diva2:441271