A natural language based search engine for ICD10 diagnosis encoding.
We have developed a multiple step process for implementing an ICD10 search engine. The complexity of the task has been shown and we recommend collecting adequate expertise before starting any implementation. Underestimation of the expert time and inadequate data resources are probable reasons for failure. We also claim that when all conditions are met in term of resource and availability of the expertise, the benefits of a responsive ICD10 search engine will be present and the investment will be successful.
Baud
Robert
R
Service d'Informatique Médicale Hospitals of Geneva, Switzerland.
D019992:Databases as Topic; D016247:Information Storage and Retrieval; D038801:International Classification of Diseases; D009323:Natural Language Processing