http://www.cnr.it/ontology/cnr/individuo/prodotto/ID293867
A Coding Support System for the ICD-9-CM standard (Contributo in atti di convegno)
- Type
- Label
- A Coding Support System for the ICD-9-CM standard (Contributo in atti di convegno) (literal)
- Anno
- 2014-01-01T00:00:00+01:00 (literal)
- Alternative label
Roberto Guarasci, Erika Pasceri, Maria Teresa Chiaravalloti, Vincenzo Lagani, Roberto Trunfio (2014)
A Coding Support System for the ICD-9-CM standard
in ICHI 2014 - IEEE International Conference on Healthcare Informatics, VERONA
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Roberto Guarasci, Erika Pasceri, Maria Teresa Chiaravalloti, Vincenzo Lagani, Roberto Trunfio (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- IIT-CNR, Università della Calabria, CNR- ICAR, Institute of Computer Science, Foundation for Research and Technology - Hellas (FORTH) (literal)
- Titolo
- A Coding Support System for the ICD-9-CM standard (literal)
- Abstract
- The 9th revision of the International Classificationof Diseases (as modified in the United States, also known as ICD-9-CM) is one of the most widely used classification system forclinical problems and procedures. Electronic health recordsystems usually require physicians to encode diagnoses ormedical procedures through appropriate ICD-9-CM codes. SinceICD-9-CM is constituted by more than 13,000 diagnosis and3,500 procedure codes, clearly the coding procedure is a nontrivialtask. In this paper we present a coding support system forassisting physicians in the assignment of ICD-9-CM codes. Theproposed system is based on tools for natural languageprocessing and text-searches on knowledge bases. It analyzes ashort medical text and then returns the list of ICD-9-CM codesthat are most likely to be associated with the given text. A set ofnumerical experiments were carried out in order to assess thereliability and quality of the proposed support system. Theresults of the experiments show that the support system has anaccuracy of 92%, largely improving physicians' average performances. (literal)
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