Explainable ICD multi-label classification of EHRs in Spanish with convolutional attention. (January 2022)

• Record Type: Journal Article
• Authors: Trigueros, Owen; Blanco, Alberto; Lebeña, Nuria; Casillas, Arantza; Pérez, Alicia
• Is Part Of: International journal of medical informatics. Volume 157(2022)
• Publication Date: 2022-01
• Subjects: Deep Neural Understanding -- Clinical Language Processing -- Electronic Health Records -- International Classification of Diseases -- Decision Support Systems; 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.2021.104615
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: Convolutional networks with attention mechanisms allow explainable predictions. Attention mechanisms can be employed to design Decision Support Systems. Explainable models allow the temporal ordering of diagnoses. Abstract: Background: This work deals with Natural Language Processing applied to Electronic Health Records (EHRs). EHRs are coded following the International Classification of Diseases (ICD) leading to a multi-label classification problem. Previously proposed approaches act as black-boxes without giving further insights. Explainable Artificial Intelligence (XAI) helps to clarify what brought the model to make the predictions. Goal: This work aims to obtain explainable predictions of the diseases and procedures contained in EHRs. As an application, we show visualizations of the attention stored and propose a prototype of a Decision Support System (DSS) that highlights the text that motivated the choice of each of the proposed ICD codes. Methods: Convolutional Neural Networks (CNNs) with attention mechanisms were used. Attention mechanisms allow to detect which part of the input (EHRs) motivate the output (medical codes), producing explainable predictions. Results: We successfully applied methods in a Spanish corpus getting challenging results. Finally, we presented the idea of extracting the chronological order of the ICDs in a given EHR by anchoring the codes to different stages of the clinical admission. Conclusions: We found that explainable deep learning models applied to predict medical codes store helpful information that could be used to assist medical experts while reaching a solid performance. In particular, we show that the information stored in the attention mechanisms enables DSS and a shallow chronology of diagnoses.