Fraunhofer AICOS at CLEF eHealth 2020 Task 1: Clinical Code Extraction From Textual Data Using Fine-Tuned BERT Models

Nosology is an important branch of Medical Science that concerns the classification and coding of diseases, conditions, procedures, and other medical information. This is a vital task for all stakeholders of the health sector, from hospitals and health regulators, to insurance companies and governments. The ICD10 system is the current revision of a Nosology system managed by the World Health Organization, being widely used internationally. Since medical coding is based on manual analysis of clinical textual data, it is ripe for automation, with Natural Language Processing (NLP) techniques used to address this challenge. This paper describes our contribution to the CLEF eHealth 2020 Task 1 Challenge, regarding Information Extraction of ICD10 codes on unstructured Spanish clinical text. We present two approaches for ICD10 code extraction based on Conditional Random Fields (CRFs) and the BERT Deep Learning Language Model. The BERT -based methodology achieved a mean average precision of 0.517 and 0.445 for ICD10-CM and ICD10-PCS codes, respectively, and a F1 score of 0.505 for the Explainable AI subtask. The results obtained show the flexibility and robustness of pre-trained Deep Learning models for NLP, only requiring fine-tuning for a particular task, leading to reduced requirements both for labelled data and computational effort.