Výsledky vyhledávání - "Liu, Emerson"

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Interpretation of Intracardiac Electrograms Through Textual Representations

Autor: Han, William Jongwon, Gomez, Diana, Alok, Avi, Duan, Chaojing, Rosenberg, Michael A., Weber, Douglas, Liu, Emerson, Zhao, Ding

Understanding the irregular electrical activity of atrial fibrillation (AFib) has been a key challenge in electrocardiography. For serious cases of AFib, catheter ablations are performed to collect intracardiac electrograms (EGMs). EGMs offer intrica

Externí odkaz: http://arxiv.org/abs/2402.01115

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Automated Cardiovascular Record Retrieval by Multimodal Learning between Electrocardiogram and Clinical Report

Autor: Qiu, Jielin, Zhu, Jiacheng, Liu, Shiqi, Han, William, Zhang, Jingqi, Duan, Chaojing, Rosenberg, Michael, Liu, Emerson, Weber, Douglas, Zhao, Ding

Automated interpretation of electrocardiograms (ECG) has garnered significant attention with the advancements in machine learning methodologies. Despite the growing interest, most current studies focus solely on classification or regression tasks, wh

Externí odkaz: http://arxiv.org/abs/2304.06286

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Transfer Knowledge from Natural Language to Electrocardiography: Can We Detect Cardiovascular Disease Through Language Models?

Autor: Qiu, Jielin, Han, William, Zhu, Jiacheng, Xu, Mengdi, Rosenberg, Michael, Liu, Emerson, Weber, Douglas, Zhao, Ding

Recent advancements in Large Language Models (LLMs) have drawn increasing attention since the learned embeddings pretrained on large-scale datasets have shown powerful ability in various downstream applications. However, whether the learned knowledge

Externí odkaz: http://arxiv.org/abs/2301.09017

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GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction

Autor: Zhu, Jiacheng, Qiu, Jielin, Yang, Zhuolin, Weber, Douglas, Rosenberg, Michael A., Liu, Emerson, Li, Bo, Zhao, Ding

Publikováno v: Machine Learning for Healthcare 2022, JMLR Volume 182

There has been an increased interest in applying deep neural networks to automatically interpret and analyze the 12-lead electrocardiogram (ECG). The current paradigms with machine learning methods are often limited by the amount of labeled data. Thi

Externí odkaz: http://arxiv.org/abs/2208.01220

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Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation

Autor: Qiu, Jielin, Zhu, Jiacheng, Xu, Mengdi, Huang, Peide, Rosenberg, Michael, Weber, Douglas, Liu, Emerson, Zhao, Ding

In this paper, we focus on a new method of data augmentation to solve the data imbalance problem within imbalanced ECG datasets to improve the robustness and accuracy of heart disease detection. By using Optimal Transport, we augment the ECG disease

Externí odkaz: http://arxiv.org/abs/2202.00567

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