This is not an official awesome, just a collection of some public available resources about ICD medical coding.
A similar awesome list of medical coding can be found here: acadTags/Awesome-medical-coding-NLP
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- Paper-with-codes leaderboard of MIMIC-III benchmarks of ICD-9 medical coding.
- icd10data: Free 2022 ICD-10-CM/PCS Medical Coding Reference
- Larkey, L. S., & Croft, W. B. (1995). Automatic assignment of icd9 codes to discharge summaries. Technical report, University of Massachusetts at Amherst, Amherst, MA.
- Larkey, L. S., & Croft, W. B. (1996, August). Combining classifiers in text categorization. In Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 289-297).
- Crammer, K., Dredze, M., Ganchev, K., Talukdar, P., & Carroll, S. (2007, June). Automatic code assignment to medical text. In Biological, translational, and clinical language processing (pp. 129-136).
- Pestian, J., Brew, C., Matykiewicz, P., Hovermale, D. J., Johnson, N., Cohen, K. B., & Duch, W. (2007, June). A shared task involving multi-label classification of clinical free text. In Biological, translational, and clinical language processing (pp. 97-104).
- Farkas, R., & Szarvas, G. (2008, April). Automatic construction of rule-based ICD-9-CM coding systems. In BMC bioinformatics (Vol. 9, No. 3, pp. 1-9). BioMed Central.
- Stanfill, M. H., Williams, M., Fenton, S. H., Jenders, R. A., & Hersh, W. R. (2010). A systematic literature review of automated clinical coding and classification systems. Journal of the American Medical Informatics Association, 17(6), 646-651.
- Perotte, A., Pivovarov, R., Natarajan, K., Weiskopf, N., Wood, F., Elhadad, N., 2013. Diagnosis code assignment: models and evaluation metrics. Journal of the American Medical Informatics Association 21, 231–237. URL: https://doi.org/10.1136/amiajnl-2013-002159, doi:10.1136/amiajnl-2013-002159.
- Subotin, M., & Davis, A. (2014, June). A system for predicting ICD-10-PCS codes from electronic health records. In Proceedings of BioNLP 2014 (pp. 59-67).
- (ICD-10-PCS, dataset unknown, micro-averaged Fscore is 0.485)
- Lin, C., Hsu, C. J., Lou, Y. S., Yeh, S. J., Lee, C. C., Su, S. L., & Chen, H. C. (2017). Artificial intelligence learning semantics via external resources for classifying diagnosis codes in discharge notes. Journal of medical Internet research, 19(11), e8344.
- (ICD-10-CM, use 103,390 discharge notes in the Tri-Service General Hospital in Taipei, mean F-measure 0.9086)
- Berndorfer, S., & Henriksson, A. (2017). Automated diagnosis coding with combined text representations. Stud Health Technol Inform, 235, 201-205.
- Shi, H., Xie, P., Hu, Z., Zhang, M., & Xing, E. P. (2017). Towards automated ICD coding using deep learning. arXiv preprint arXiv:1711.04075.
- Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. arXiv preprint arXiv:1802.05365.
- Rios, A., & Kavuluru, R. (2018, October). Few-shot and zero-shot multi-label learning for structured label spaces. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing (Vol. 2018, p. 3132). NIH Public Access.
- Mullenbach, J., Wiegreffe, S., Duke, J., Sun, J., & Eisenstein, J. (2018). Explainable prediction of medical codes from clinical text. arXiv preprint arXiv:1802.05695.
- Baumel, T., Nassour-Kassis, J., Cohen, R., Elhadad, M., & Elhadad, N. (2018, June). Multi-label classification of patient notes: case study on ICD code assignment. In Workshops at the thirty-second AAAI conference on artificial intelligence.
- Xiao, C., Choi, E., & Sun, J. (2018). Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. Journal of the American Medical Informatics Association, 25(10), 1419-1428.
- Xie, X., Xiong, Y., Yu, P. S., & Zhu, Y. (2019, November). Ehr coding with multi-scale feature attention and structured knowledge graph propagation. In Proceedings of the 28th ACM international conference on information and knowledge management (pp. 649-658).
- Kaur, R., University., W.S., 2018. A comparative analysis of selected set of natural language processing (NLP) and machine learning (ML) algorithms for clinical coding using clinical classification standards. URL: http://hdl.handle.net/1959.7/uws:49614. a thesis presented to Western Sydney University, School of Computing, Engineering and Mathematics, in fulfilment of the requirements for the degree of Master of Research
- Rios, A., & Kavuluru, R. (2019). Neural transfer learning for assigning diagnosis codes to EMRs. Artificial intelligence in medicine, 96, 116-122. 27 Beltagy, I., Peters, M. E., & Cohan, A. (2020). Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150.
- Zhang, Z., Liu, J., & Razavian, N. (2020). BERT-XML: Large scale automated ICD coding using BERT pretraining. arXiv preprint arXiv:2006.03685.
- Vu, T., Nguyen, D. Q., & Nguyen, A. (2020). A label attention model for icd coding from clinical text. arXiv preprint arXiv:2007.06351.
- Li, F., & Yu, H. (2020, April). ICD coding from clinical text using multi-filter residual convolutional neural network. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 05, pp. 8180-8187).
- Teng, F., Yang, W., Chen, L., Huang, L., & Xu, Q. (2020). Explainable prediction of medical codes with knowledge graphs. Frontiers in Bioengineering and Biotechnology, 8, 867.
- Almagro, M., Unanue, R. M., Fresno, V., & Montalvo, S. (2020). ICD-10 coding of Spanish electronic discharge summaries: an extreme classification problem. IEEE Access, 8, 100073-100083.
- (CIE-10-ES: Spanish modification of ICD-10)
- López-García, G., Jerez, J. M., Ribelles, N., Alba, E., & Veredas, F. J. (2021). Transformers for clinical coding in spanish. IEEE Access, 9, 72387-72397.
- Sen, C., Ye, B., Aslam, J., & Tahmasebi, A. (2021). From Extreme Multi-label to Multi-class: A Hierarchical Approach for Automated ICD-10 Coding Using Phrase-level Attention. arXiv preprint arXiv:2102.09136.
- Dong, H., Suárez-Paniagua, V., Whiteley, W., & Wu, H. (2021). Explainable automated coding of clinical notes using hierarchical label-wise attention networks and label embedding initialisation. Journal of biomedical informatics, 116, 103728.
- Gao, S., Alawad, M., Young, M. T., Gounley, J., Schaefferkoetter, N., Yoon, H. J., ... & Tourassi, G. (2021). Limitations of transformers on clinical text classification. IEEE journal of biomedical and health informatics, 25(9), 3596-3607.
- Blanco, A., Pérez, A., & Casillas, A. (2021). Exploiting ICD Hierarchy for Classification of EHRs in Spanish Through Multi-Task Transformers. IEEE Journal of Biomedical and Health Informatics, 26(3), 1374-1383.
- Kaur, R., Ginige, J. A., & Obst, O. (2021). A Systematic Literature Review of Automated ICD Coding and Classification Systems using Discharge Summaries. arXiv preprint arXiv:2107.10652.
- Kim, B. H., & Ganapathi, V. (2021, October). Read, attend, and code: Pushing the limits of medical codes prediction from clinical notes by machines. In Machine Learning for Healthcare Conference (pp. 196-208). PMLR.
- Wu, Y., Zeng, M., Fei, Z., Yu, Y., Wu, F. X., & Li, M. (2022). Kaicd: A knowledge attention-based deep learning framework for automatic icd coding. Neurocomputing, 469, 376-383.
- Taylor, N., Zhang, Y., Joyce, D., Nevado-Holgado, A., & Kormilitzin, A. (2022). Clinical Prompt Learning with Frozen Language Models. arXiv preprint arXiv:2205.05535.