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Election of Diagnosis Codes: Words as Responsible Citizens
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2011 (English)Conference paper (Refereed)
Abstract [en]

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 , 2011.
Keyword [en]
Diagnosis Code Assignment, ICD-10, Random Indexing, Electronic Patient Records
National Category
Information Science
Research subject
Computer and Systems Sciences
URN: urn:nbn:se:su:diva-62346OAI: diva2:441271
Available from: 2011-09-15 Created: 2011-09-15

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