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Wetzel, \u201cDirectly modeling missing data in sequences with rnns: Improved classification of clinical time series,\u201d in Machine learning for healthcare conference. PMLR, 2016, pp. 253\u2013270."},{"key":"null","unstructured":"S. N. Shukla and B. M. Marlin, \u201cInterpolation-prediction networks for irregularly sampled time series,\u201d arXiv preprint arXiv:1909.07782, 2019."},{"key":"null","unstructured":"X. Zhang, M. Zeman, T. Tsiligkaridis, and M. Zitnik, \u201cGraph-guided network for irregularly sampled multivariate time series,\u201d arXiv preprint arXiv:2110.05357, 2021."},{"key":"null","unstructured":"M. Horn, M. Moor, C. Bock, B. Rieck, and K. Borgwardt, \u201cSet functions for time series,\u201d in International Conference on Machine Learning. PMLR, 2020, pp. 4353\u20134363."},{"key":"null","unstructured":"Y. Rubanova, R. T. Chen, and D. K. Duvenaud, \u201cLatent ordinary differential equations for irregularly-sampled time series,\u201d Advances in neural information processing systems, vol. 32, 2019."},{"key":"null","unstructured":"G. Zerveas, S. Jayaraman, D. Patel, A. Bhamidipaty, and C. Eickhoff, \u201cA transformer-based framework for multivariate time series representation learning,\u201d in Proceedings of the 27 th  ACM SIGKDD conference on knowledge discovery & data mining, 2021, pp. 2114\u20132124."},{"key":"null","unstructured":"J. Che, L. Tang, S. Deng, and X. Su, \u201cChinese word segmentation based on bidirectional gru-crf model,\u201d International Journal of Performability Engineering, vol. 14, no. 12, p. 3066, 2018."},{"key":"null","unstructured":"S. N. Shukla and B. M. Marlin, \u201cMulti-time attention networks for irregularly sampled time series,\u201d arXiv preprint arXiv:2101.10318, 2021."},{"key":"null","unstructured":"S. N. Shukla and B. M. Marin, \u201cHeteroscedastic temporal variational autoencoder for irregularly sampled time series,\u201d arXiv preprint arXiv:2107.11350, 2021."},{"key":"null","unstructured":"Y. Meng, W. Speier, M. K. Ong, and C. W. Arnold, \u201cBidirectional representation learning from transformers using multimodal electronic health record data to predict depression,\u201d IEEE journal of biomedical and health informatics, vol. 25, no. 8, pp. 3121\u20133129, 2021."},{"key":"null","unstructured":"Q. Suo, F. Ma, Y. Yuan, M. Huai, W. Zhong, J. Gao, and A. Zhang, \u201cDeep patient similarity learning for personalized healthcare,\u201d IEEE transactions on nanobioscience, vol. 17, no. 3, pp. 219\u2013227, 2018."},{"key":"null","unstructured":"Z. Yang, Y. Liu, C. Ouyang, L. Ren, and W. Wen, \u201cCounterfactual can be strong in medical question and answering,\u201d Information Processing & Management, vol. 60, no. 4, p. 103408, 2023."},{"key":"null","unstructured":"W. Wen, Y. Liu, C. Ouyang, Q. Lin, and T. Chung, \u201cEnhanced prototypical network for few-shot relation extraction,\u201d Information Processing & Management, vol. 58, no. 4, p. 102596, 2021."},{"key":"null","unstructured":"C. Long, Y. Liu, C. Ouyang, and Y. Yu, \u201cBailicai: A domain-optimized retrieval-augmented generation framework for medical applications,\u201d arXiv preprint arXiv:2407.21055, 2024."},{"key":"null","unstructured":"Z. Tao, C. Ouyang, Y. Liu, T. Chung, and Y. Cao, \u201cMulti-head attention graph convolutional network model: End-to-end entity and relation joint extraction based on multi-head attention graph convolutional network,\u201d CAAI Transactions on Intelligence Technology, vol. 8, no. 2, pp. 468\u2013477, 2023."},{"key":"null","unstructured":"L. Ren, Y. Liu, C. Ouyang, Y. Yu, S. Zhou, Y. He, and Y. Wan, \u201cDylas: A dynamic label alignment strategy for large-scale multi-label text classification,\u201d Information Fusion, vol. 120, p. 103081, 2025."},{"key":"null","unstructured":"Y.-H. H. Tsai, S. Bai, P. P. Liang, J. Z. Kolter, L.-P. Morency, and R. Salakhutdinov, \u201cMultimodal transformer for unaligned multimodal language sequences,\u201d in Proceedings of the conference. Association for computational linguistics. Meeting, vol. 2019. NIH Public Access, 2019, p. 6558."},{"key":"null","unstructured":"A. Vaswani, \u201cAttention is all you need,\u201d Advances in Neural Information Processing Systems, 2017."},{"key":"null","unstructured":"E. Choi, M. T. Bahadori, A. Schuetz, W. F. Stewart, and J. Sun, \u201cDoctor ai: Predicting clinical events via recurrent neural networks,\u201d in Machine learning for healthcare conference. PMLR, 2016, pp. 301\u2013318."},{"key":"null","unstructured":"P. Gupta, P. Malhotra, L. Vig, and G. Shroff, \u201cTransfer learning for clinical time series analysis using recurrent neural networks,\u201d arXiv preprint arXiv:1807.01705, 2018."},{"key":"null","unstructured":"A. E. Johnson, T. J. Pollard, L. Shen, L.-w. H. Lehman, M. Feng, M. Ghassemi, B. Moody, P. Szolovits, L. Anthony Celi, and R. G. Mark, \u201cMimic-iii, a freely accessible critical care database,\u201d Scientific data, vol. 3, no. 1, pp. 1\u20139, 2016."},{"key":"null","unstructured":"H. Harutyunyan, H. Khachatrian, D. C. Kale, G. Ver Steeg, and A. Galstyan, \u201cMultitask learning and benchmarking with clinical time series data,\u201d Scientific data, vol. 6, no. 1, p. 96, 2019."},{"key":"null","unstructured":"S. Khadanga, K. Aggarwal, S. Joty, and J. Srivastava, \u201cUsing clinical notes with time series data for icu management,\u201d arXiv preprint arXiv:1909.09702, 2019."},{"key":"null","unstructured":"S. Tipirneni and C. K. Reddy, \u201cSelf-supervised transformer for sparse and irregularly sampled multivariate clinical time-series,\u201d ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 16, no. 6, pp. 1\u201317, 2022."},{"key":"null","unstructured":"S. Cui, J. Wang, Y. Zhong, H. Liu, T. Wang, and F. Ma, \u201cAutomated fusion of multimodal electronic health records for better medical predictions,\u201d in Proceedings of the 2024 SIAM International Conference on Data Mining (SDM). SIAM, 2024, pp. 361\u2013369."},{"key":"null","unstructured":"F. Wang, F. Wu, Y. Tang, and L. Yu, \u201cCtpd: Cross-modal temporal pattern discovery for enhanced multimodal electronic health records analysis,\u201d arXiv preprint arXiv:2411.00696, 2024."},{"key":"null","unstructured":"W. Yao, K. Yin, W. K. Cheung, J. Liu, and J. 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