Search

We describe an approach to select semantically coherent specialty subsets based on the historical use of terminology by different service areas. Our approach uses rule-based and machine learning techniques to obtain a reduced set of 29 specialties. [ABSTRACT FROM AUTHOR]

ICD-10 (International Classification of Diseases 10th revision) is a classification code for diseases, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or diseases. This paper describes an automatic information retrieval approach to map free-text disease descriptions to ICD-10 codes. We use the Hospital Italiano de Buenos Aires (HIBA) terminology data mapped to ICD-10 codes as indexed data to find an appropriate ICD-10 code using search engine similarity metrics. [ABSTRACT FROM AUTHOR]

ICD-10 (International Classification of Diseases 10th revision) is a classification code for diseases, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or diseases. This paper describes an automatic information retrieval approach to map free-text disease descriptions to ICD-10 codes. We use the Hospital Italiano de Buenos Aires (HIBA) terminology data mapped to ICD-10 codes as indexed data to find an appropriate ICD-10 code using search engine similarity metrics.

Clinical terms are noisy descriptions typed by healthcare professionals in Spanish language in the electronic health record system (EHR). Thus, an evaluation of terminology search engine that extends SNOMED CT and an approach that uses historical data of clinical terms is described. We show how to measure precision and recall using historical search data, and we show how the performance of the search engine can be improved significantly using the technology available in the search engine.