Interpretable deep learning to map diagnostic texts to ICD-10 codes. (September 2019)

• Record Type: Journal Article
• Authors: Atutxa, Aitziber; de Ilarraza, Arantza Díaz; Gojenola, Koldo; Oronoz, Maite; Perez-de-Viñaspre, Olatz
• Is Part Of: International journal of medical informatics. Volume 129(2019)
• Page Start: 49
• Page End: 59
• Publication Date: 2019-09
• Subjects: International Classification of Diseases -- Electronic health records -- Sequence-to-sequence mapping -- Neural machine translation; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2019.05.015
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: Automatic extraction of morbid disease or conditions contained in death certificates is extremely useful for standardization, alleviating and smoothing human work. The positive impact of standardization is specially relevant for epidemiological studies, comparison across physicians, hospitals and countries and also for billing purposes. General and multilingual approach to render diagnostic terms in death certificates into the standard framework provided by the ICD. Automatic coding of diagnostic terms treated as an automatic translation task. Study of the impact of different neural architectures on sequence-to-sequence ICD-10 coding. Our results give a new state of the art on multilingual ICD-10 coding, outperforming several alternative approaches. Informative ICD-10 coding, interpretable by clinicians. Abstract: 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.