Explainable ICD multi-label classification of EHRs in Spanish with convolutional attention

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.

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.

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.

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.

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.