Let Your Heart Speak in its Mother Tongue: Multilingual Captioning of Cardiac Signals

TL;DR

This paper introduces a deep neural network that automatically generates multilingual clinical reports from cardiac signals, leveraging a novel multilingual pre-training method to improve report diversity and accuracy.

Contribution

It presents a new multilingual captioning model for cardiac signals and a novel pre-training task that enhances performance without extensive labeled data.

Findings

Multilingual models outperform monolingual counterparts.

The proposed pre-training rivals state-of-the-art methods.

Generated reports are diverse and clinically plausible.

Abstract

Cardiac signals, such as the electrocardiogram, convey a significant amount of information about the health status of a patient which is typically summarized by a clinician in the form of a clinical report, a cumbersome process that is prone to errors. To streamline this routine process, we propose a deep neural network capable of captioning cardiac signals; it receives a cardiac signal as input and generates a clinical report as output. We extend this further to generate multilingual reports. To that end, we create and make publicly available a multilingual clinical report dataset. In the absence of sufficient labelled data, deep neural networks can benefit from a warm-start, or pre-training, procedure in which parameters are first learned in an arbitrary task. We propose such a task in the form of discriminative multilingual pre-training where tokens from clinical reports are randomly replaced with those from other languages and the network is tasked with predicting the language of all tokens. We show that our method performs on par with state-of-the-art pre-training methods such as MLM, ELECTRA, and MARGE, while simultaneously generating diverse and plausible clinical reports. We also demonstrate that multilingual models can outperform their monolingual counterparts, informally terming this beneficial phenomenon as the blessing of multilinguality.