Boosting ICD multi-label classification of health records with contextual embeddings and label-granularity.

Background And Objective: This work deals with clinical text mining, a field of Natural Language Processing applied to biomedical informatics. The aim is to classify Electronic Health Records with respect to the International Classification of Diseases, which is the foundation for the identification of international health statistics, and the standard for reporting diseases and health conditions. Within the framework of data mining, the goal is the multi-label classification, as each health record has assigned multiple International Classification of Diseases codes.

Interpretable deep learning to map diagnostic texts to ICD-10 codes.

Background: Automatic extraction of morbid disease or conditions contained in Death Certificates is a critical process, useful for billing, epidemiological studies and comparison across countries. The fact that these clinical documents are written in regular natural language makes the automatic coding process difficult because, often, spontaneous terms diverge strongly from standard reference terminology such as the International Classification of Diseases (ICD).

Objective: Our aim is to propose a general and multilingual approach to render Diagnostic Terms into the standard framework provided by the ICD.

Neural machine translation of clinical texts between long distance languages.

Objective: To analyze techniques for machine translation of electronic health records (EHRs) between long distance languages, using Basque and Spanish as a reference. We studied distinct configurations of neural machine translation systems and used different methods to overcome the lack of a bilingual corpus of clinical texts or health records in Basque and Spanish.

Materials And Methods: We trained recurrent neural networks on an out-of-domain corpus with different hyperparameter values.

SNOMED CT in a language isolate: an algorithm for a semiautomatic translation.

Background: The Systematized Nomenclature of Medicine--Clinical Terms (SNOMED CT) is officially released in English and Spanish. In the Basque Autonomous Community two languages, Spanish and Basque, are official. The first attempt to semi-automatically translate the SNOMED CT terminology content to Basque, a less resourced language is presented in this paper.