Election of Diagnosis Codes Words as Responsible Citizens
Aron Henriksson, Martin Hassel · 2011
Abstract. 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 domainspecific 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.