基于文本和心电图图像的跨模态深度学习在急诊科急性胸痛患者风险预测中的应用

2021 IEEE 3rd Eurasia Conference on IOT, Communication and Engineering (ECICE) Pub Date : 2021-10-29 DOI:10.1109/ECICE52819.2021.9645629

Po Hsiang Lin, J. Hsieh, Chien-Hua Chen, J. Jeng

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引用次数: 0

摘要

急性胸痛是急诊科最常见的主诉之一，经常与危及生命的疾病有关。我们的目的是构建一个跨模态深度学习模型，通过医生的临床文献和心电图(ECG)来预测急性胸痛的风险。两种不同的模式包括最初的心电图图像和医生的笔记被用来预测处置。

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Cross-Modal Deep Learning Based on Texts and ECG Images for Risk Prediction of Patients with Acute Chest Pain in the Emergency Department

Acute chest pain is one of the most common complaints and is frequently related to life-threatening diseases in the emergency department. We aimed to construct a cross-modal deep learning model for risk prediction of acute chest pain by the physicians' clinical texts and electrocardiogram (ECG). Two different modalities included the initial ECG image and the physicians' notes are used to predict the disposition.

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2021 IEEE 3rd Eurasia Conference on IOT, Communication and Engineering (ECICE)

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