EXAMINATION OF SUMMARIZED MEDICAL RECORDS FOR ICD CODE CLASSIFICATION VIA BERT
Dilek AYDOGAN-KILIC
Aalborg University, Department of Materials and Production, Operations Research Group (Denmark)
https://orcid.org/0000-0002-9194-9400
Deniz Kenan KILIC
denizkk@mp.aau.dkArray (Denmark)
https://orcid.org/0000-0002-6996-3425
Izabela Ewa NIELSEN
Aalborg University, Department of Materials and Production, Operations Research Group (Denmark)
https://orcid.org/0000-0002-3506-2741
Abstract
The International Classification of Diseases (ICD) is utilized by member countries of the World Health Organization (WHO). It is a critical system to ensure worldwide standardization of diagnosis codes, which enables data comparison and analysis across various nations. The ICD system is essential in supporting payment systems, healthcare research, service planning, and quality and safety management. However, the sophisticated and intricate structure of the ICD system can sometimes cause issues such as longer examination times, increased training expenses, a greater need for human resources, problems with payment systems due to inaccurate coding, and unreliable data in health research. Additionally, machine learning models that use automated ICD systems face difficulties with lengthy medical notes. To tackle this challenge, the present study aims to utilize Medical Information Mart for Intensive Care (MIMIC-III) medical notes that have been summarized using the term frequency-inverse document frequency (TF-IDF) method. These notes are further analyzed using deep learning, specifically bidirectional encoder representations from transformers (BERT), to classify disease diagnoses based on ICD codes. Even though the proposed methodology using summarized data provides lower accuracy performance than state-of-the-art methods, the performance results obtained are promising in terms of continuing the study of extracting summary input and more important features, as it provides real-time ICD code classification and more explainable inputs.
Keywords:
Artificial intelligence, Natural language processing (NLP), Classification problem, International classification of diseases (ICD), Bidirectional Encoder Representations from Transformers (BERT), MIMIC-IIIReferences
Alsentzer, E., Murphy, J. R., Boag, W., Weng, W. H., Jin, D., Naumann, T., & McDermott, M. (2019). Publicly available clinical BERT embeddings. arXiv preprint. https://doi.org/10.48550/arXiv.1904.03323
DOI: https://doi.org/10.18653/v1/W19-1909
Google Scholar
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.
Google Scholar
Bhargava, P., Drozd, A., & Rogers, A. (2021). Generalization in NLI: Ways (not) to go beyond simple heuristics. arXiv preprint. https://doi.org/10.48550/arXiv.2110.01518
DOI: https://doi.org/10.18653/v1/2021.insights-1.18
Google Scholar
Cao, P., Chen, Y., Liu, K., Zhao, J., Liu, S., & Chong, W. (2020a, July). HyperCore: Hyperbolic and co-graph representation for automatic ICD coding. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3105-3114. https://doi.org/10.18653/v1/2020.acl-main.282
DOI: https://doi.org/10.18653/v1/2020.acl-main.282
Google Scholar
Cao, P., Yan, C., Fu, X., Chen, Y., Liu, K., Zhao, J., Liu, S., & Chong, W. (2020b, July). Clinical-coder: Assigning interpretable ICD-10 codes to Chinese clinical notes. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 294-301. https://doi.org/10.18653/v1/2020.acl-demos.33
DOI: https://doi.org/10.18653/v1/2020.acl-demos.33
Google Scholar
Chen, P. F., Wang, S. M., Liao, W. C., Kuo, L. C., Chen, K. C., Lin, Y. C., Yang, C., Chiu, C., Chang, S., & Lai, F. (2021). Automatic ICD-10 coding and training system: deep neural network based on supervised learning. JMIR Medical Informatics, 9(8), e23230. https://doi.org/10.2196/23230
DOI: https://doi.org/10.2196/23230
Google Scholar
Chute, C. G., & Çelik, C. (2021). Overview of ICD-11 architecture and structure. BMC medical informatics and decision making, 21(6), 1-7. https://doi.org/10.1186/s12911-021-01539-1
DOI: https://doi.org/10.1186/s12911-021-01539-1
Google Scholar
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint. https://doi.org/10.48550/arXiv.1810.04805
Google Scholar
Du, Y., Xu, T., Ma, J., Cen, E., Zheng, Y., Liu, T., & Tong, G. (2020). An automatic ICD coding method for clinical records based on deep neural network. Big Data Res, 6(5), 3-15. https://doi.org/10.11959/j.issn.2096-0271.2020040
Google Scholar
Farkas, R., & Szarvas, G. (2008). Automatic construction of rule-based ICD-9-CM coding systems. BMC bioinformatics, 9 Suppl 3(Suppl 3), S10. https://doi.org/10.1186/1471-2105-9-S3-S10
DOI: https://doi.org/10.1186/1471-2105-9-S3-S10
Google Scholar
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., Mietus, J. E., Moody, G. B., Peng, C. K., & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
DOI: https://doi.org/10.1161/01.CIR.101.23.e215
Google Scholar
Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J. & Poon, H. (2021). Domain-specific language model pretraining for biomedical natural language processing. ACM Transactions on Computing for Healthcare (HEALTH), 3(1), 1-23. https://doi.org/10.1145/3458754
DOI: https://doi.org/10.1145/3458754
Google Scholar
Harrison, J. E., Weber, S., Jakob, R., & Chute, C. G. (2021). ICD-11: an international classification of diseases for the twenty-first century. BMC medical informatics and decision making, 21(6), 1-10. https://doi.org/10.1186/s12911-021-01534-6
DOI: https://doi.org/10.1186/s12911-021-01534-6
Google Scholar
Hsu, J. L., Hsu, T. J., Hsieh, C. H., & Singaravelan, A. (2020). Applying convolutional neural networks to predict the ICD-9 codes of medical records. Sensors, 20(24), 7116. https://doi.org/10.3390/s20247116
DOI: https://doi.org/10.3390/s20247116
Google Scholar
Huang, C. W., Tsai, S. C., & Chen, Y. N. (2022). PLM-ICD: automatic ICD coding with pretrained language models. arXiv preprint. https://doi.org/10.48550/arXiv.2207.05289
DOI: https://doi.org/10.18653/v1/2022.clinicalnlp-1.2
Google Scholar
Huang, J., Osorio, C., & Sy L. W. (2019). An empirical evaluation of deep learning for ICD-9 code assignment using MIMIC-III clinical notes. Computer Methods and Programs in Biomedicine, 177, 141–153. https://doi.org/10.1016/j.cmpb.2019.05.024
DOI: https://doi.org/10.1016/j.cmpb.2019.05.024
Google Scholar
Johnson, A., Pollard, T., & Mark, R. (2016a). MIMIC-III Clinical Database (version 1.4). PhysioNet. https://doi.org/10.13026/C2XW26
Google Scholar
Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L. W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., & Mark, R. G. (2016b). MIMIC-III, a freely accessible critical care database. Scientific data, 3(1), 1-9. https://doi.org/10.1038/sdata.2016.35
DOI: https://doi.org/10.1038/sdata.2016.35
Google Scholar
Kaur, R., & Ginige, J. A. (2018). Comparative analysis of algorithmic approaches for auto-coding with ICD-10-AM and ACHI. Studies in health technology and informatics, 252, 73-79. https://doi.org/10.3233/978-1-61499-890-7-73
Google Scholar
Kaur, R., Ginige, J. A., & Obst, O. (2021). A systematic literature review of automated ICD coding and classification systems using discharge summaries. arXiv preprint. https://doi.org/10.48550/arXiv.2107.10652
Google Scholar
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, 34(05), pp. 8180-8187. https://doi.org/10.1609/aaai.v34i05.6331
DOI: https://doi.org/10.1609/aaai.v34i05.6331
Google Scholar
Li, M., Fei, Z., Zeng, M., Wu, F. X., Li, Y., Pan, Y., & Wang, J. (2019). Automated ICD-9 coding via a deep learning approach. IEEE/ACM transactions on computational biology and bioinformatics, 16(4), 1193-1202. https://doi.org/10.1109/TCBB.2018.2817488
DOI: https://doi.org/10.1109/TCBB.2018.2817488
Google Scholar
Marafino, B. J., Davies, J. M., Bardach, N. S., Dean, M. L., & Dudley, R. A. (2014). N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit. Journal of the American Medical Informatics Association, 21(5), 871-875. https://doi.org/10.1136/amiajnl-2014-002694
DOI: https://doi.org/10.1136/amiajnl-2014-002694
Google Scholar
Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022). Explainable artificial intelligence: a comprehensive review. Artificial Intelligence Review, 1-66. https://doi.org/10.1007/s10462-021-10088-y
DOI: https://doi.org/10.1007/s10462-021-10088-y
Google Scholar
Moons, E., Khanna, A., Akkasi, A., & Moens, M. F. (2020). A comparison of deep learning methods for ICD coding of clinical records. Applied Sciences, 10(15), 5262. https://doi.org/10.3390/app10155262
DOI: https://doi.org/10.3390/app10155262
Google Scholar
Mullenbach, J., Wiegreffe, S., Duke, J., Sun, J., & Eisenstein, J. (2018). 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, New Orleans, Louisiana. Association for Computational Linguistics. https://doi.org/10.18653/v1/N18-1100
DOI: https://doi.org/10.18653/v1/N18-1100
Google Scholar
Nawalkar, N., Attar, V. Z., & Kalamkar, S. P. (2022). Automated icd-9 medical code assignment from given free text using deep learning approach. In Advances in Data and Information Sciences: Proceedings of ICDIS 2021 (pp. 317-327). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-16-5689-7_28
DOI: https://doi.org/10.1007/978-981-16-5689-7_28
Google Scholar
Pascual, D., Luck, S., & Wattenhofer, R. (2021). Towards BERT-based automatic ICD coding: Limitations and opportunities. arXiv preprint. https://doi.org/10.48550/arXiv.2104.06709
DOI: https://doi.org/10.18653/v1/2021.bionlp-1.6
Google Scholar
Perotte, A., Pivovarov, R., Natarajan, K., Weiskopf, N., Wood, F., & Elhadad, N. (2014). Diagnosis code assignment: models and evaluation metrics. Journal of the American Medical Informatics Association, 21(2), 231-237. https://doi.org/10.1136/amiajnl-2013-002159
DOI: https://doi.org/10.1136/amiajnl-2013-002159
Google Scholar
Pezzella, P. (2022). The ICD‐11 is now officially in effect. World Psychiatry, 21(2), 331.8. https://doi.org/10.1002/wps.20982
DOI: https://doi.org/10.1002/wps.20982
Google Scholar
Ponthongmak, W., Thammasudjarit, R., McKay, G. J., Attia, J., Theera-Ampornpunt, N., & Thakkinstian, A. (2023). Development and external validation of automated ICD-10 coding from discharge summaries using deep learning approaches. Informatics in Medicine Unlocked, 38, 101227. https://doi.org/10.1016/j.imu.2023.101227
DOI: https://doi.org/10.1016/j.imu.2023.101227
Google Scholar
Rios, A., & Kavuluru, R. (2018). Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces. Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing, 2018, 3132–3142. NIH Public Access.
DOI: https://doi.org/10.18653/v1/D18-1352
Google Scholar
Scheurwegs, E., Luyckx, K., Luyten, L., Daelemans, W., & Van den Bulcke, T. (2016). Data integration of structured and unstructured sources for assigning clinical codes to patient stays. Journal of the American Medical Informatics Association, 23(e1), e11-e19. https://doi.org/10.1093/jamia/ocv115
DOI: https://doi.org/10.1093/jamia/ocv115
Google Scholar
Shi, H., Xie, P., Hu, Z., Zhang, M., & Xing, E. P. (2017). Towards automated ICD coding using deep learning. arXiv preprint. https://doi.org/10.48550/arXiv.1711.04075
Google Scholar
Singaravelan, A., Hsieh, C. H., Liao, Y. K., & Hsu, J. L. (2021). Predicting icd-9 codes using self-report of patients. Applied Sciences, 11(21), 10046. https://doi.org/10.3390/app112110046
DOI: https://doi.org/10.3390/app112110046
Google Scholar
Tabassum, A., & Patil, R. R. (2020). A survey on text pre-processing & feature extraction techniques in natural language processing. International Research Journal of Engineering and Technology (IRJET), 7(06), 4864-4867.
Google Scholar
Teng, F., Liu, Y., Li, T., Zhang, Y., Li, S., & Zhao, Y. (2022). A review on deep neural networks for ICD coding. IEEE Transactions on Knowledge and Data Engineering, 35(5), 4357-4375. https://doi.org/10.1109/TKDE.2022.3148267
DOI: https://doi.org/10.1109/TKDE.2022.3148267
Google Scholar
Turc, I., Chang, M. W., Lee, K., & Toutanova, K. (2019). Well-read students learn better: The impact of student initialization on knowledge distillation. arXiv preprint. https://doi.org/10.48550/arXiv.1908.08962
Google Scholar
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30. Curran Associates, Inc.
Google Scholar
Vu, T., Nguyen, D. Q., & Nguyen, A. (2020). A label attention model for ICD coding from clinical text. arXiv preprint. https://doi.org/10.48550/arXiv.2007.06351
DOI: https://doi.org/10.24963/ijcai.2020/461
Google Scholar
Wang, D., Su, J., & Yu, H. (2020). Feature Extraction and Analysis of Natural Language Processing for Deep Learning English Language. IEEE Access, 8, 46335-46345. https://doi.org/10.1109/ACCESS.2020.2974101
DOI: https://doi.org/10.1109/ACCESS.2020.2974101
Google Scholar
Wang, G., Li, C., Wang, W., Zhang, Y., Shen, D., Zhang, X., Henao, R., & Carin, L. (2018). Joint embedding of words and labels for text classification. arXiv preprint. https://doi.org/10.48550/arXiv.1805.04174
DOI: https://doi.org/10.18653/v1/P18-1216
Google Scholar
Wu, X., Zhao, Y., Yang, Y., Liu, Z., & Clifton, D. A. (2022). A Comparison of Representation Learning Methods for Medical Concepts in MIMIC-IV. medRxiv, 2022-08. http://dx.doi.org/10.2139/ssrn.4583878
DOI: https://doi.org/10.1101/2022.08.21.22278835
Google Scholar
Yan, C., Fu, X., Liu, X., Zhang, Y., Gao, Y., Wu, J., & Li, Q. (2022). A survey of automated International Classification of Diseases coding: development, challenges, and applications. Intelligent Medicine, 2(3), 161-173. https://doi.org/10.1016/j.imed.2022.03.003
DOI: https://doi.org/10.1016/j.imed.2022.03.003
Google Scholar
Zeng, M., Li, M., Fei, Z., Yu, Y., Pan, Y., & Wang, J. (2019). Automatic ICD-9 coding via deep transfer learning. Neurocomputing, 324, 43-50. https://doi.org/10.1016/j.neucom.2018.04.081
DOI: https://doi.org/10.1016/j.neucom.2018.04.081
Google Scholar
Zhang, Z., Liu, J., & Razavian, N. (2020). BERT-XML: Large scale automated ICD coding using BERT pretraining. arXiv preprint. https://doi.org/10.48550/arXiv.2006.03685
DOI: https://doi.org/10.18653/v1/2020.clinicalnlp-1.3
Google Scholar
Authors
Dilek AYDOGAN-KILICAalborg University, Department of Materials and Production, Operations Research Group Denmark
https://orcid.org/0000-0002-9194-9400
Authors
Izabela Ewa NIELSENAalborg University, Department of Materials and Production, Operations Research Group Denmark
https://orcid.org/0000-0002-3506-2741
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