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BioSignal Copilot: Leveraging the power of LLMs in drafting reports for biomedical signals. medRxiv (2023), 2023-06."},{"key":"e_1_3_2_2_35_1","volume-title":"Zero-shot ecg classification with multimodal learning and test-time clinical knowledge enhancement. arXiv preprint arXiv:2403.06659","author":"Liu Che","year":"2024","unstructured":"Che Liu, Zhongwei Wan, Cheng Ouyang, Anand Shah, Wenjia Bai, and Rossella Arcucci. 2024b. Zero-shot ecg classification with multimodal learning and test-time clinical knowledge enhancement. arXiv preprint arXiv:2403.06659 (2024)."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.emnlp-main.567"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671575"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107187"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3698587.3701447"},{"key":"e_1_3_2_2_40_1","volume-title":"Self-supervised representation learning from 12-lead ECG data. Computers in biology and medicine","author":"Mehari Temesgen","year":"2022","unstructured":"Temesgen Mehari and Nils Strodthoff. 2022. Self-supervised representation learning from 12-lead ECG data. Computers in biology and medicine, Vol. 141 (2022), 105114."},{"key":"e_1_3_2_2_41_1","volume-title":"Artificial intelligence for the electrocardiogram. 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In 2017 International joint conference on neural networks (IJCNN)","author":"Wang Zhiguang","year":"2017","unstructured":"Zhiguang Wang, Weizhong Yan, and Tim Oates. 2017. Time series classification from scratch with deep neural networks: A strong baseline. In 2017 International joint conference on neural networks (IJCNN). IEEE, 1578-1585.","journal-title":"IEEE"},{"key":"e_1_3_2_2_64_1","volume-title":"Denny Zhou, et al.","author":"Wei Jason","year":"2022","unstructured":"Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al., 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, Vol. 35 (2022), 24824-24837."},{"key":"e_1_3_2_2_65_1","volume-title":"Bloomberggpt: A large language model for finance. arXiv preprint arXiv:2303.17564","author":"Wu Shijie","year":"2023","unstructured":"Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, and Gideon Mann. 2023. Bloomberggpt: A large language model for finance. arXiv preprint arXiv:2303.17564 (2023)."},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3006707"},{"key":"e_1_3_2_2_67_1","unstructured":"Jiaxian Yan Jintao Zhu Yuhang Yang Qi Liu Kai Zhang Zaixi Zhang Xukai Liu Boyan Zhang Kaiyuan Gao Jinchuan Xiao et al. 2025. BioMiner: A Multi-modal System for Automated Mining of Protein-Ligand Bioactivity Data from Literature. bioRxiv (2025) 2025-04."},{"key":"e_1_3_2_2_68_1","volume-title":"CCF Conference on Big Data. Springer, 102-120","author":"Yu Hao","year":"2024","unstructured":"Hao Yu, Aoran Gan, Kai Zhang, Shiwei Tong, Qi Liu, and Zhaofeng Liu. 2024a. Evaluation of retrieval-augmented generation: A survey. In CCF Conference on Big Data. Springer, 102-120."},{"key":"e_1_3_2_2_69_1","first-page":"650","article-title":"Zero-shot ECG diagnosis with large language models and retrieval-augmented generation. In Machine learning for health (ML4H)","author":"Yu Han","year":"2023","unstructured":"Han Yu, Peikun Guo, and Akane Sano. 2023. Zero-shot ECG diagnosis with large language models and retrieval-augmented generation. In Machine learning for health (ML4H). PMLR, 650-663.","journal-title":"PMLR"},{"key":"e_1_3_2_2_70_1","volume-title":"Ecg semantic integrator (esi): A foundation ecg model pretrained with llm-enhanced cardiological text. arXiv preprint arXiv:2405.19366","author":"Yu Han","year":"2024","unstructured":"Han Yu, Peikun Guo, and Akane Sano. 2024b. Ecg semantic integrator (esi): A foundation ecg model pretrained with llm-enhanced cardiological text. arXiv preprint arXiv:2405.19366 (2024)."},{"key":"e_1_3_2_2_71_1","volume-title":"Florence: A new foundation model for computer vision. arXiv preprint arXiv:2111.11432","author":"Yuan Lu","year":"2021","unstructured":"Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, et al., 2021. Florence: A new foundation model for computer vision. arXiv preprint arXiv:2111.11432 (2021)."},{"key":"e_1_3_2_2_72_1","volume-title":"CAD-Editor","author":"Yuan Yu","year":"2025","unstructured":"Yu Yuan, Shizhao Sun, Qi Liu, and Jiang Bian. 2025. CAD-Editor: A Locate-then-Infill Framework with Automated Training Data Synthesis for Text-Based CAD Editing. arXiv preprint arXiv:2502.03997 (2025)."},{"key":"e_1_3_2_2_73_1","volume-title":"Wide residual networks. arXiv preprint arXiv:1605.07146","author":"Zagoruyko Sergey","year":"2016","unstructured":"Sergey Zagoruyko and Nikos Komodakis. 2016. Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)."},{"key":"e_1_3_2_2_74_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01759"},{"key":"e_1_3_2_2_75_1","first-page":"1","article-title":"Maefe: Masked autoencoders family of electrocardiogram for self-supervised pretraining and transfer learning","volume":"72","author":"Zhang Huaicheng","year":"2022","unstructured":"Huaicheng Zhang, Wenhan Liu, Jiguang Shi, Sheng Chang, Hao Wang, Jin He, and Qijun Huang. 2022. Maefe: Masked autoencoders family of electrocardiogram for self-supervised pretraining and transfer learning. IEEE Transactions on Instrumentation and Measurement, Vol. 72 (2022), 1-15.","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"e_1_3_2_2_76_1","volume-title":"MLBF-Net: A multi-lead-branch fusion network for multi-class arrhythmia classification using 12-lead ECG","author":"Zhang Jing","year":"2021","unstructured":"Jing Zhang, Deng Liang, Aiping Liu, Min Gao, Xiang Chen, Xu Zhang, and Xun Chen. 2021a. MLBF-Net: A multi-lead-branch fusion network for multi-class arrhythmia classification using 12-lead ECG. IEEE journal of translational engineering in health and medicine, Vol. 9 (2021), 1-11."},{"key":"e_1_3_2_2_77_1","first-page":"377","article-title":"Eatn: An efficient adaptive transfer network for aspect-level sentiment analysis","volume":"35","author":"Zhang Kai","year":"2021","unstructured":"Kai Zhang, Qi Liu, Hao Qian, Biao Xiang, Qing Cui, Jun Zhou, and Enhong Chen. 2021b. Eatn: An efficient adaptive transfer network for aspect-level sentiment analysis. IEEE Transactions on Knowledge and Data Engineering, Vol. 35, 1 (2021), 377-389.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_2_78_1","volume-title":"Self-supervised time series representation learning via cross reconstruction transformer","author":"Zhang Wenrui","year":"2023","unstructured":"Wenrui Zhang, Ling Yang, Shijia Geng, and Shenda Hong. 2023. Self-supervised time series representation learning via cross reconstruction transformer. IEEE Transactions on Neural Networks and Learning Systems (2023)."},{"key":"e_1_3_2_2_79_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i7.28567"},{"key":"e_1_3_2_2_80_1","first-page":"1","article-title":"Classification of cardiac abnormalities from ECG signals using SE-ResNet. 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