Automatic classification of clinical cases using deep learning techniques

Bryan Cataldo-Vivar, Hector Allende-Cid, Rodrigo Alfaro

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The automatic assignation of disease codes is a complex problem that has been addressed many times throughout decades. In particular, the categorization of ICD (International Classification of Diseases) codes, which it s a compendium of symptoms, diseases, procedures and injuries. This activity is done by manually analyzing clinical cases or discharge summaries and its use has spread to areas like billing, administration or refund. Leading to associated costs close to $417 billion dollars for United States on 2012. Therefore in this investigation we propose Deep Learning models aiming to help in the task of code assignment. For this, 6 models are proposed, including architectures of Convulutional and Recurrent Neuronal Networks; both focused on NLP (Natural Language Processing) extracting features through aWord Embeddings approach. The results were obtained from the top 10, 20, 50 and 100 most frequent diseases; getting an Average Precision of 79,86% for the top 10 with an AUC of 91,37% which outperforms other methods used previously in this task.

Original languageEnglish
Title of host publicationIET Conference Proceedings
PublisherInstitution of Engineering and Technology
Number of pages6
ISBN (Electronic)9781839534300, 9781839535048, 9781839535741, 9781839535918, 9781839536045, 9781839536052, 9781839536069, 9781839536199, 9781839536366, 9781839536588, 9781839536793, 9781839536809, 9781839536816, 9781839536847, 9781839537035
StatePublished - 2021
Event11th International Conference of Pattern Recognition Systems, ICPRS 2021 - Virtual, Online
Duration: 17 Mar 202119 Mar 2021


Conference11th International Conference of Pattern Recognition Systems, ICPRS 2021
CityVirtual, Online


  • Deep learning
  • Disease code assignment
  • Natural language processing
  • Word embeddings


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