GPT-Enhanced Hierarchical Deep Learning Model for Automated ICD Coding

GPT-Enhanced Hierarchical Deep Learning Model for Automated ICD Coding

Volume 9, Issue 4, Page No 21-34, 2024

Author’s Name: Joshua Carberry, Haiping Xu

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Computer and Information Science Department, University of Massachusetts Dartmouth, Dartmouth, MA 02747, USA

a)whom correspondence should be addressed. E-mail: hxu@umassd.edu

Adv. Sci. Technol. Eng. Syst. J. 9(4), 21-34(2024); a  DOI: 10.25046/aj090404

Keywords: ICD coding, Deep learning, GPT-4 prompt, Fine-grained data point, Hierarchical classification

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Abstract

In healthcare, accurate communication is critical, and medical coding, especially coding using the ICD (International Classification of Diseases) standards, plays a vital role in achieving this accuracy. Traditionally, ICD coding has been a time-consuming manual process performed by trained professionals, involving the assignment of codes to patient records, such as doctor’s notes. In this paper, we present an automated ICD coding approach using deep learning models and demonstrate the feasibility and effectiveness of the approach across subsets of ICD codes. The proposed method employs a fine-grained approach that individually predicts the appropriate medical code for each diagnosis. In order to utilize sufficient evidence to enhance the classification capabilities of our deep leaning models, we integrate GPT-4 to extract semantically related sentences for each diagnosis from doctor’s notes. Furthermore, we introduce a hierarchical classifier to handle the large label space and complex classification inherent in the ICD coding task. This hierarchical approach decomposes the ICD coding task into smaller, more manageable subclassification tasks, thereby improving tractability and addressing the challenges posed by the high number of unique labels associated with ICD coding.

Received: 22 April 2024, Revised: 01 July 2024, Accepted: 04 July 2024, Published Online: 18 July 2024

References (33)

  1. WHO, “International statistical classification of diseases and related health problems (ICD),” Health Topics, World Health Organization (WHO), January 1, 2022. Retrieved on December 12, 2023 from: https://www.who.int /standards/classifications/classification-of-diseases
  2. J. Carberry, H. Xu, “Fine-grained ICD code assignment using ontology-based classification,” In Proceedings of the 2022 IEEE 23rd International Conference on Information Reuse and Integration for Data Science (IRI), 228-233, San Diego, CA, USA, August 2022, doi: 10.1109/ IRI54793.2022.00058.
  3. I. Goldstein, A. Arzumtsyan, O. Uzuner, “Three approaches to automatic assignment of ICD-9-CM codes to radiology reports,” AMIA Annual Symposium Proceedings, no. 1, 279-283, 2007.
  4. R. Farkas, G. Szarvas, “Automatic construction of rule-based ICD-9-CM coding systems,” BMC Bioinformatics, 9(suppl 3, S10), April 2008, doi: 10.1186/1471-2105-9-S3-S10.
  5. J. Medori, C. Fairon, “Machine learning and features selection for semi-automatic ICD-9-CM encoding,” In Proceedings of the NAACL HLT 2nd Louhi Workshop Text Data Mining Health Documents, 84-89, Los Angeles, June 2010.
  6. M. Li, Z. Fei, F. Wu, Y. Li, Y. Pan, J. Wang, “Automated ICD-9 coding via a deep learning approach,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16(4), pp. 1193-1202, July-August 2019, doi: 10.1109/TCBB.2018.2817488.
  7. T. S. Heo, Y. Yoo, Y. Park, B. Jo, K. Lee, K. Kim, “Medical code prediction from discharge summary: document to sequence BERT using sequence attention,” In Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA), 1239-1244, Pasadena, CA, USA, 2021, doi: 10.1109/ICMLA52953.2021.00201.
  8. J. Carberry, H. Xu, “A hierarchical fine-grained deep learning model for automated medical coding,” In Proceedings of the 3rd International Conference on Computing and Machine Intelligence (ICMI 2024), Central Michigan University, Michigan, USA, April 13-14, 2024.
  9. A. E. W. Johnson, T. J. Pollard, L. Shen, L. H. Lehman, M. Feng, M. Ghassemi, B. Moody, P. Szolovits, L. Anthony Celi, R. G. Mark, “MIMIC-III, a freely accessible critical care database,” Scientific Data, 3(1), 160035, 2016, doi: doi.org/10.1038/sdata.2016.35.
  10. P. Chen, S. Wang, W. Laio, L. Kuo, K. Chen, Y. Lin, C. Yang, C. Chiu, S. Chang, F. Lai, “Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning,” JMIR Med Inform, 9(8) :e23230, August 2021, doi: 10.2196/23230.
  11. Y. Wu, Z. Chen, X. Yao, X. Chen, Z. Zhou, J. Xue, “JAN: Joint attention networks for automatic ICD coding,” IEEE Journal of Biomedical and Health Informatics, 26(10), 5235-5246, October 2022, doi: 10.1109/JBHI.2022.3189404.
  12. V. Mayya, S. S. Kamath, V. Sugumaran, “LATA – Label attention transformer architectures for ICD-10 coding of unstructured clinical notes,” In Proceedings of the 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 1-7, Melbourne, Australia, 2021, doi: 10.1109/CIBCB49929.2021.9562815.
  13. Y. Dong, “A deep learning-driven disease classification method using MIMIC-III database and natural language processing,” In Proceedings of the 2023 IEEE 3rd International Conference on Data Science and Computer Application (ICDSCA), 1068-1072, Dalian, China, October 2023, doi: 10.1109/ICDSCA59871.2023.10392478.
  14. M. Marin, L. E. Sucar, J. A. Gonzales, R. Diaz, “A hierarchical model for morphological galaxy classification,” In Proceedings of the Twenty-Sixth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2013), 438-443, St. Pete Beach, FL, USA, May 22-24, 2013.
  15. M. Ramírez-Corona, L. E. Sucar, E. F. Morales, “Hierarchical multi-label classification based on path evaluation,” International Journal of Approximate Reasoning, 68, 179-183, January 2016.
  16. A. D. Secker, M. N. Davies, A. A. Freitas, J. Timmis, M. Mendao, D. R. Flower, “An experimental comparison of classification algorithms for the hierarchical prediction of protein function,” Expert Update (Magazine of the British Computer Society’s Specialist Group on AI), 9(3), 17-22, 2007.
  17. K. Wang, S. Zhou, Y. He, “Hierarchical classification of real life documents,” In Proceedings of the 2001 SIAM International Conference on Data Mining (SDM01), 1-16, Chicago, IL, USA, April 5-7, 2001, doi: 10.1137/1.9781611972719.22.
  18. K. Daisey, S. D. Brown, “Effects of the hierarchy in hierarchical, multi-label classification,” Chemometrics and Intelligent Laboratory Systems, 207, 104177, December 2020, doi: 10.1016/j.chemolab.2020.104177.
  19. C. N. Silla Jr., A. A. Freitas, “A global-model naive bayes approach to the hierarchical prediction of protein functions,” In Proceedings of the 9th IEEE International Conference on Data Mining, 992-997, December 6-9, 2009, Miami Beach, FL, USA, doi: 10.1109/ICDM.2009.85.
  20. S. Lawrence, C. L. Giles, A. C. Tsoi, A. D. Back, “Face recognition: a convolutional neural-network approach,” IEEE Transactions on Neural Networks, 8(1), 98-113, January 1997, doi: 10.1109/72.554195.
  21. X. Peng, T. Tan, T. Fan, “Automatic ICD coding based on bias removal,” In Proceedings of the 2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT), 53-57, Jilin, China, 2024, doi: 10.1109/ICCECT60629.2024.10546138.
  22. T. Chomutare, A. Budrionis, H. Dalianis, “Combining deep learning and fuzzy logic to predict rare ICD-10 codes from clinical notes,” In Proceedings of the 2022 IEEE International Conference on Digital Health (ICDH), 163-168, Barcelona, Spain, July 2022, doi: 10.1109/ICDH55609.2022.00033.
  23. A. Kumar, S. Das, S. Roy, “Transfer learning improves unsupervised assignment of ICD codes with clinical notes,” In Proceedings of the 2023 IEEE International Conference on Digital Health (ICDH), 278-287, Chicago, IL, USA, July 2023, doi: 10.1109/ICDH60066.2023.00047.
  24. N. T. K. Le, N. Hadiprodjo, H. El-Alfy, A. Kerimzhanov, A. Teshebaev, “The recent large language models in NLP,” In Proceedings of the 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 1-6, Sydney, Australia, October 2023, doi: 10.1109/ISCIT57293.2023.10376050.
  25. J. Fields, K. Chovanec, P. Madiraju, “A survey of text classification with transformers: how wide? how large? how long? how accurate? how expensive? how safe?” IEEE Access, 12, 6518-6531, 2024, doi: 10.1109/ACCESS.2024.3349952.
  26. C. Sun, X. Qiu, Y. Xu, X. Huang, “How to fine-tune BERT for text classification?” arXiv:1905.05583 [cs.CL], February 2020, Verson 3, doi: 10.48550/arXiv.1905.05583.
  27. J. Devlin, M. Chang, K. Lee, K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” arXiv: 1810.04805 [cs.CL], May 2019, Version 2, doi: 10.48550/arXiv.1810.04805.
  28. F. Karl, A. Scherp, “Transformers are short text classifiers: a study of inductive short text classifiers on benchmarks and real-world datasets,” arXiv:2211.16878v3 [cs.CL], 2023, doi: 10.48550/ arXiv.2211.16878.
  29. K. M. Yoo, D. Park, J. Kang, S. Lee, W. Park, “GPT3Mix: leveraging large-scale language models for text augmentation,” arXiv:2104.08826 [cs.CL], April 2021, doi: 10.48550/arXiv.2104.08826.
  30. X. Cai, M. Xiao, Z. Ning, Y. Zhou, “Resolving the imbalance issue in hierarchical disciplinary topic inference via LLM-based data augmentation,” In Proceedings of the 2023 IEEE International Conference on Data Mining (ICDM), 956-961, Shanghai, China, December 2023, doi: 10.1109/ ICDM58522.2023.00107.
  31. D. W. Otter, J. R. Medina, J. K. Kalita, “A survey of the usages of deep learning for natural language processing,” IEEE Transactions on Neural Networks and Learning Systems, 32(2), 604-624, February 2021, doi: 10.1109/TNNLS.2020.2979670.
  32. C. Vasantharajan, K. Z. Tun, H. Thi-Nga, S. Jain, T. Rong, C. E. Siong, “MedBERT: a pre-trained language model for biomedical named entity recognition,” In Proceedings of the 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 1482-1488, Chiang Mai, Thailand, November 2022, doi: 10.23919/APSIPAASC55919.2022.9980157.
  33. D. Brin, V. Sorin, A. Vaid, B. S. Glicksberg, A. W. Charney, G. Nadkarni, E. Klang, “Comparing ChatGPT and GPT-4 performance in USMLE soft skill assessments,” Scientific Reports, 13, 16492, 2023, doi: 10.1038/s41598-023-43436-9.

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