[4] Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Shengping Liu, and Weifeng Chong. HyperCore: Hyperbolic and co-graph representation for automatic ICD coding. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3105-3114, 2020.
[5] Xiang Dai, Ilias Chalkidis, Sune Darkner, and Desmond Elliott. Revisiting transformer-based models for long document classification. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 7212-7230, 2022.
[6] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, 2019.
[7] Hang Dong, Víctor Suárez-Paniagua, Huayu Zhang, Minhong Wang, Emma Whitfield, and Honghan Wu. Rare disease identification from clinical notes with ontologies and weak supervision. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pages 2294–2298. IEEE, 2021.
[8] Chufan Gao, Mononito Goswami, Jieshi Chen, and Artur Dubrawski. Classifying unstructured clinical notes via automatic weak supervision. In Machine Learning for Healthcare Conference, pages 673-690. PMLR, 2022.
[9] Chao-Wei Huang, Shang-Chi Tsai, and Yun-Nung Chen. PLM-ICD: Automatic ICD coding with pretrained language models. In Proceedings of the 4th Clinical Natural Language Processing Workshop, pages 10–20, 2022.
[10] Shaoxiong Ji, Matti Höttä, and Pekka Marttinen. Does the magic of BERT apply to medical code assignment? a quantitative study. Computers in biology and medicine, 139:104998, 2021.
[11] Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman Li-wei, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. Mimic-iii, a freely accessible critical care database. Scientific data, 3:160035, 2016.
[12] Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. Bart: Denoising sequence-to-sequence pretraining for natural language generation, translation, and comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7871-7880, 2020.
[13] Fei Li and Hong Yu. ICD coding from clinical text using multi-filter residual convolutional neural network. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 8180-8187, 2020.
[14] Yang Liu, Hua Cheng, Russell Klopfer, Matthew R Gormley, and Thomas Schaaf. Effective convolutional attention network for multi-label clinical document classification. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5941-5953, 2021.
[15] Chang Lu, Tian Han, and Yue Ning. Context-aware health event prediction via transition functions on dynamic disease graphs. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 4567-4574, 2022.
[16] Chang Lu, Chandan K Reddy, Prithwish Chakraborty, Samantha Kleinberg, and Yue Ning. Collaborative graph learning with auxiliary text for temporal event prediction in healthcare. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, pages 3529–3535. International Joint Conferences on Artificial Intelligence Organization, 8 2021. Main Track.
[17] Laxmaiah Manchikanti. Implications of fraud and abuse in interventional pain management. Pain Physician, 5(3):320, 2002.
[18] James Mullenbach, Sarah Wiegrefe, Jon Duke, Jimeng Sun, and Jacob Eisenstein. Explainable prediction of medical codes from clinical text. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1101-1111, 2018.