Interpretable deep learning to map diagnostic texts to ICD-10 codes
| Autor: | Maite Oronoz, Arantza Díaz de Ilarraza, Koldo Gojenola, Aitziber Atutxa, Olatz Perez-de-Viñaspre |
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| Rok vydání: | 2018 |
| Předmět: |
020205 medical informatics
Artificial neural network business.industry Computer science Deep learning ICD-10 Health Informatics 02 engineering and technology computer.software_genre Terminology 03 medical and health sciences 0302 clinical medicine Deep Learning International Classification of Diseases 0202 electrical engineering electronic engineering information engineering Electronic Health Records 030212 general & internal medicine Artificial intelligence Neural Networks Computer business computer Natural language Natural language processing Coding (social sciences) |
| Zdroj: | International journal of medical informatics. 129 |
| ISSN: | 1872-8243 |
| Popis: | 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. We have evaluated our proposal on a set of clinical texts written in French, Hungarian and Italian. Methods ICD-10 encoding is a multi-class classification problem with an extensive (thousands) number of classes. After considering several approaches, we tackle our objective as a sequence-to-sequence task. According to current trends, we opted to use neural networks. We tested different types of neural architectures on three datasets in which Diagnostic Terms (DTs) have their ICD-10 codes associated. Results and conclusions Our results give a new state-of-the art on multilingual ICD-10 coding, outperforming several alternative approaches, and showing the feasibility of automatic ICD-10 prediction obtaining an F-measure of 0.838, 0.963 and 0.952 for French, Hungarian and Italian, respectively. Additionally, the results are interpretable, providing experts with supporting evidence when confronted with coding decisions, as the model is able to show the alignments between the original text and each output code. |
| Databáze: | OpenAIRE |
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