AI-based ICD coding and classification approaches using discharge summaries: A systematic literature review

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000097 · Cited by 64,042 · see citing works

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · 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) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works

Glove: Global Vectors for Word Representation

Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · see citing works

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · see citing works

Distributed Representations of Words and Phrases and their Compositionality

Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533 · see citing works

A survey on transfer learning

Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works

Convolutional Neural Networks for Sentence Classification

Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831 · see citing works

Representation Learning: A Review and New Perspectives

Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Lu, Z. Q. J. (2010) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,904 · see citing works

A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection

Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909 · see citing works

Distributed Representations of Sentences and DocumentsPreprint

Le, Q. V., & Mikolov, T. (2014) · International Conference on Machine Learning · doi:10.48550/arxiv.1405.4053 · Cited by 10,307 · see citing works

A systematic analysis of performance measures for classification tasks

Sokolova, M., & Lapalme, G. (2009) · Information Processing & Management · doi:10.1016/j.ipm.2009.03.002 · Cited by 7,515 · see citing works

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz682 · Cited by 7,218 · see citing works

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank

Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1170 · Cited by 6,878 · see citing works

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

(2016) · doi:10.1145/2939672 · Cited by 6,563 · see citing works

Hierarchical Attention Networks for Document Classification

Yang, Z., Yang, D., Dyer, C., He, X., Smola, A. J., & Hovy, E. H. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1174 · Cited by 4,874 · see citing works

2013 IEEE International Conference on Computer Vision

(2013) · doi:10.1109/iccv19984.2013 · Cited by 4,232 · see citing works

Introduction to Semi-Supervised Learning

Zhu, X., & Goldberg, A. B. (2009) · Synthesis lectures on artificial intelligence and machine learning · doi:10.1007/978-3-031-01548-9 · Cited by 1,849 · see citing works

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

(2021) · doi:10.18653/v1/2021.naacl-main · Cited by 1,723 · see citing works

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

(2018) · doi:10.18653/v1/d18-1 · Cited by 1,688 · see citing works

The Lack of A Priori Distinctions Between Learning Algorithms

Wolpert, D. H. (1996) · Neural Computation · doi:10.1162/neco.1996.8.7.1341 · Cited by 1,652 · see citing works

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Abualsaud, M., & Smucker, M. (2019) · doi:10.1145/3357384 · Cited by 1,299 · see citing works

Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

(2013) · doi:10.18653/v1/d13-1 · Cited by 1,296 · see citing works

A Primer on Neural Network Models for Natural Language Processing

Goldberg, Y. (2016) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4992 · Cited by 1,259 · see citing works

Large-Scale Long-Tailed Recognition in an Open World

Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., & Yu, S. X. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00264 · Cited by 1,169 · see citing works

A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries

Chapman, W. W., Bridewell, W. B., Hanbury, P., Cooper, G. F., & Buchanan, B. G. (2001) · Journal of Biomedical Informatics · doi:10.1006/jbin.2001.1029 · Cited by 1,121 · see citing works

Feature Generating Networks for Zero-Shot Learning

Xian, Y., Lorenz, T., Schiele, B., & Akata, Z. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00581 · Cited by 1,080 · see citing works

Publicly Available Clinical BERT EmbeddingsPreprint

Alsentzer, E., Murphy, J. R., Boag, W., Weng, W., Jin, D., Naumann, T., & McDermott, M. B. A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.03323 · Cited by 1,040 · see citing works

HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition

Yan, Z., Zhang, H., Piramuthu, R., Jagadeesh, V., DeCoste, D., Di, W., & Yu, Y. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.314 · Cited by 936 · see citing works

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

(2015) · doi:10.18653/v1/d15-1 · Cited by 818 · see citing works

Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

(2016) · doi:10.18653/v1/n16-1 · Cited by 724 · see citing works

Systematic review of discharge coding accuracy

Burns, E. M., Rigby, E., Mamidanna, R., Bottle, A., Aylin, P. P., Ziprin, P., & Faiz, O. D. (2011) · Journal of Public Health · doi:10.1093/pubmed/fdr054 · Cited by 699 · see citing works

Explainable Prediction of Medical Codes from Clinical Text

Mullenbach, J., Wiegreffe, S., Duke, J., Sun, J., & Eisenstein, J. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1100 · Cited by 597 · see citing works

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

(2020) · doi:10.18653/v1/2020.acl-main · Cited by 551 · see citing works

Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

(2018) · doi:10.18653/v1/n18-1 · Cited by 539 · see citing works

A Tutorial on Multilabel Learning

Gibaja, E., & Ventura, S. (2015) · ACM Computing Surveys · doi:10.1145/2716262 · Cited by 530 · see citing works

Disorders related to sexuality and gender identity in the ICD-11: revising the ICD-10 classification based on current scientific evidence, best clinical practices, and human rights considerations

Reed, G. M., Drescher, J., Krueger, R. B., Atalla, E., Cochran, S. D., First, M. B., Cohen‐Kettenis, P. T., Arango‐de Montis, I., Parish, S. J., Cottler, S., et al. (2016) · World Psychiatry · doi:10.1002/wps.20354 · Cited by 469 · see citing works

Joint Embedding of Words and Labels for Text Classification

Wang, G., Li, C., Wang, W., Zhang, Y., Shen, D., Zhang, X., Henao, R., & Carin, L. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1216 · Cited by 461 · see citing works

Combining classifiers in text categorization

Larkey, L. S., & Croft, W. B. (1996) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/243199.243276 · Cited by 416 · see citing works

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

(2020) · doi:10.24963/ijcai.2020 · Cited by 414 · see citing works

Multi-modal Cycle-Consistent Generalized Zero-Shot Learning

Felix, R., Kumar, B. G. V., Reid, I. D., & Carneiro, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01231-1_2 · Cited by 391 · see citing works

A shared task involving multi-label classification of clinical free text

Pestian, J. P., Brew, C., Matykiewicz, P., Hovermale, D. J., Johnson, N. D., Cohen, K. B., & Duch, W. (2007) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.3115/1572392.1572411 · Cited by 376 · see citing works

A systematic review of discharge coding accuracy

Campbell, S., Campbell, M., Grimshaw, J., & Walker, A. E. (2001) · Journal of Public Health · doi:10.1093/pubmed/23.3.205 · Cited by 300 · see citing works

Label Embedding Trees for Large Multi-Class Tasks

Bengio, S., Weston, J., & Grangier, D. (2011) · Cited by 290 · see citing works

Data Processing and Text Mining Technologies on Electronic Medical Records: A Review

Sun, W., Cai, Z., Li, Y., Liu, F., Fang, S., & Wang, G. (2018) · Journal of Healthcare Engineering · doi:10.1155/2018/4302425 · Cited by 287 · see citing works

Learning from Multiple Partially Observed Views - an Application to Multilingual Text Categorization

Amini, M.-R., Usunier, N., & Goutte, C. (2009) · NPARC · Cited by 270 · see citing works

Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Barzilay, R., & Kan, M.-Y. (2017) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p17-1 · Cited by 269 · see citing works

Overview of Supervised Learning

Hastie, T., Tibshirani, R. J., & Friedman, J. (2008) · Springer series in statistics · doi:10.1007/978-0-387-84858-7_2 · Cited by 258 · see citing works

Diagnosis code assignment: models and evaluation metrics

Perotte, A., Pivovarov, R., Natarajan, K., Weiskopf, N., Wood, F., & Elhadad, N. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-002159 · Cited by 244 · see citing works

Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives

Gehrmann, S., Dernoncourt, F., Li, Y., Carlson, E. T., Wu, J. T.-Y., Welt, J. A., Foote, J., Moseley, E. T., Grant, D. W., Tyler, P. D., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0192360 · Cited by 243 · see citing works

A survey of word embeddings for clinical text

Khattak, F. K., Jeblee, S., Pou-Prom, C., Abdalla, M., Meaney, C. A., & Rudzicz, F. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.yjbinx.2019.100057 · Cited by 231 · see citing works

A systematic literature review of automated clinical coding and classification systems

Stanfill, M. H., Williams, M., Fenton, S. H., Jenders, R. A., & Hersh, W. (2010) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2009.001024 · Cited by 224 · see citing works

Deep reinforcement learning for imbalanced classification

Lin, E., Chen, Q., & Qi, X. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01637-z · Cited by 223 · see citing works

Proceedings of the 2007 ACM SIGMOD international conference on Management of data

(2007) · doi:10.1145/1247480 · Cited by 219 · see citing works

Deep Neural Networks in Machine Translation: An Overview

Zhang, J., & Zong, C. (2015) · IEEE Intelligent Systems · doi:10.1109/mis.2015.69 · Cited by 199 · see citing works

Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces

Rios, A., & Kavuluru, R. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1352 · Cited by 192 · see citing works

Automatic construction of rule-based ICD-9-CM coding systems

Farkas, R., & Szarvas, G. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-s3-s10 · Cited by 185 · see citing works

ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network

Li, F., & Yu, H. Q. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6331 · Cited by 179 · see citing works

A Label Attention Model for ICD Coding from Clinical Text

Vu, T., Nguyen, D. Q., & Nguyen, A. N. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/461 · Cited by 169 · see citing works

VegFru: A Domain-Specific Dataset for Fine-Grained Visual Categorization

Hou, S., Feng, Y., & Wang, Z. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.66 · Cited by 157 · see citing works

Automatic text categorization by unsupervised learning

Ko, Y., & Seo, J. (2000) · International Conference on Computational Linguistics · doi:10.3115/990820.990886 · Cited by 150 · see citing works

ML-Net: multi-label classification of biomedical texts with deep neural networks

Du, J., Chen, Q.-Y., Peng, Y., Xiang, Y., Tao, C., & Lu, Z.-Y. (2018) · J. Am. Medical Informatics Assoc. · doi:10.1093/jamia/ocz085 · Cited by 148 · see citing works

ICD-9-CM to ICD-10-CM Codes: What? Why? How?

Cartwright, D. (2013) · Advances in Wound Care · doi:10.1089/wound.2013.0478 · Cited by 141 · see citing works

Automated ICD-9 Coding via A Deep Learning Approach

Li, M., Fei, Z., Zeng, M., Wu, F., Li, Y., Pan, Y. F., & Wang, J. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2817488 · Cited by 141 · see citing works

Multi-Label Classification of Patient Notes a Case Study on ICD Code AssignmentPreprint

Baumel, T., Nassour-Kassis, J., Cohen, R., Elhadad, M., & Elhadad, N. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.09587 · Cited by 134 · see citing works

An empirical evaluation of deep learning for ICD-9 code assignment using MIMIC-III clinical notes

Huang, J., Osorio, C., & Sy, L. W. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.05.024 · Cited by 128 · see citing works

An empirical evaluation of supervised learning approaches in assigning diagnosis codes to electronic medical records

Kavuluru, R., Rios, A., & Lu, Y. (2015) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2015.04.007 · Cited by 126 · see citing works

A Neural Architecture for Automated ICD Coding

Xie, P., & Xing, E. P. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1098 · Cited by 125 · see citing works

Clinical text classification research trends: Systematic literature review and open issues

Mujtaba, G., Shuib, L., Idris, N., Hoo, W. L., Raj, R. G., Khowaja, K., Shaikh, K., & Nweke, H. F. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.09.034 · Cited by 123 · see citing works

Towards Automated ICD Coding Using Deep LearningPreprint

Shi, H., Xie, P., Hu, Z., Zhang, M., & Xing, E. P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.04075 · Cited by 120 · see citing works

B-CNN: Branch Convolutional Neural Network for Hierarchical ClassificationPreprint

Zhu, X., & Bain, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.09890 · Cited by 117 · see citing works

History of the statistical classification of diseases and causes of death

Moriyama, I. M., Loy, R. M., Robb‐Smith, A. H. T., Rosenberg, H. M., & Hoyert, D. L. (2011) · Cited by 116 · see citing works

HyperCore: Hyperbolic and Co-graph Representation for Automatic ICD Coding

Cao, P., Chen, Y., Liu, K., Zhao, J., Liu, S., & Chong, W. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.282 · Cited by 112 · see citing works

EHR Coding with Multi-scale Feature Attention and Structured Knowledge Graph Propagation

Xie, X., Xiong, Y., Yu, P. S., & Zhu, Y. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3357897 · Cited by 111 · see citing works

Proceedings of the 2nd Clinical Natural Language Processing Workshop

(2019) · doi:10.18653/v1/w19-19 · Cited by 107 · see citing works

Automatic ICD-9 coding via deep transfer learning

Zeng, M., Li, M., Fei, Z., Yu, Y., Pan, Y. F., & Wang, J. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.04.081 · Cited by 103 · see citing works

Multimodal Machine Learning for Automated ICD CodingPreprint

Xu, K., Lam, M., Pang, J., Gao, X., Band, C., Mathur, P., Papay, F. A., Khanna, A. K., Cywiński, J. B., Maheshwari, K. K., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.13348 · Cited by 90 · see citing works

BERT-XML: Large Scale Automated ICD Coding Using BERT Pretraining

Zhang, Z., Liu, J., & Razavian, N. (2020) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2020.clinicalnlp-1.3 · Cited by 87 · see citing works

N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit

Marafino, B. J., Davies, J. M., Bardach, N. S., Dean, M. L., & Dudley, R. A. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2014-002694 · Cited by 84 · see citing works

Finding Rare Classes: Active Learning with Generative and Discriminative Models

Hospedales, T. M., Gong, S., & Xiang, T. (2011) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2011.231 · Cited by 83 · see citing works

Explainable automated coding of clinical notes using hierarchical label-wise attention networks and label embedding initialisation

Dong, H., Suárez-Paniagua, V., Whiteley, W. N., & Wu, H. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103728 · Cited by 83 · see citing works

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

(2020) · doi:10.18653/v1/2020.acl-srw · Cited by 82 · see citing works

Does the magic of BERT apply to medical code assignment? A quantitative study

Ji, S., Hölttä, M., & Marttinen, P. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104998 · Cited by 80 · see citing works

Biological, translational, and clinical language processing

Cohen, K. B., Demner‐Fushman, D., Friedman, C., Hirschman, L., & Pestian, J. P. (2007) · Cited by 78 · see citing works

Organisational Factors Affecting the Quality of Hospital Clinical Coding

Santos, S., Murphy, G., Baxter, K., & Robinson, K. M. (2008) · Health Information Management Journal · doi:10.1177/183335830803700103 · Cited by 69 · see citing works

Towards BERT-based Automatic ICD Coding: Limitations and Opportunities

Pascual, D., Luck, S., & Wattenhofer, R. P. (2021) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2021.bionlp-1.6 · Cited by 62 · see citing works

Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods

Karimi, S., Dai, X., Hassanzadeh, H., & Nguyen, A. N. (2017) · doi:10.18653/v1/w17-2342 · Cited by 59 · see citing works

Explainable Prediction of Medical Codes With Knowledge Graphs

Teng, F., Yang, W., Chen, L., Huang, L., & Xu, Q. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00867 · Cited by 53 · see citing works

Generalized Zero-Shot Text Classification for ICD Coding

Song, C., Zhang, S., Sadoughi, N., Xie, P., & Xing, E. P. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/556 · Cited by 53 · see citing works

Advances in Artificial Intelligence

Zai͏̈ane, O. R., & Zilles, S. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38457-8 · Cited by 50 · see citing works

Proceedings of the 20th Workshop on Biomedical Language Processing

(2021) · doi:10.18653/v1/2021.bionlp-1 · Cited by 50 · see citing works

Proceedings of the 3rd Clinical Natural Language Processing Workshop

(2020) · doi:10.18653/v1/2020.clinicalnlp-1 · Cited by 50 · see citing works

Selecting relevant features from the electronic health record for clinical code prediction

Scheurwegs, E., Čule, B., Luyckx, K., Luyten, L., & Daelemans, W. M. P. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.09.004 · Cited by 48 · see citing works

A Comparison of Deep Learning Methods for ICD Coding of Clinical Records

Moons, E., Khanna, A., Akkasi, A., & Moens, M. (2020) · Applied Sciences · doi:10.3390/app10155262 · Cited by 47 · see citing works

Comparative Analysis of Text Classification Approaches in Electronic Health Records

Mascio, A., Kraljević, Ž., Bean, D. M., Dobson, R., Stewart, R. J., Bendayan, R., & Roberts, A. (2020) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2020.bionlp-1.9 · Cited by 45 · see citing works

Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text

Ji, S., Cambria, E., & Marttinen, P. (2020) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2020.clinicalnlp-1.8 · Cited by 45 · see citing works

Proceedings of the Australasian Computer Science Week Multiconference

(2019) · doi:10.1145/3290688 · Cited by 44 · see citing works

Neural transfer learning for assigning diagnosis codes to EMRs

Rios, A., & Kavuluru, R. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.04.002 · Cited by 43 · see citing works

Clinical Natural Language Processing with Deep Learning

Hasan, S. A., & Farri, O. (2019) · Data Science for Healthcare · doi:10.1007/978-3-030-05249-2_5 · Cited by 43 · see citing works

Artificial Intelligence Learning Semantics via External Resources for Classifying Diagnosis Codes in Discharge Notes

Lin, C. S., Hsu, C.-J., Lou, Y.-S., Yeh, S.-J., Lee, C., Su, S., & Chen, H. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.8344 · Cited by 42 · see citing works

Towards automated clinical coding

Catling, F. J. R., Spithourakis, G., & Riedel, S. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.09.021 · Cited by 42 · see citing works

Predicting ICD-9 code groups with fuzzy similarity based supervised multi-label classification of unstructured clinical nursing notes

Gangavarapu, T., Jayasimha, A., Krishnan, G. S., & Kamath, S. S. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105321 · Cited by 40 · see citing works

Automated ICD coding via unsupervised knowledge integration (UNITE)

Sonabend, A., Cai, W., Ahuja, Y., Ananthakrishnan, A. N., Xia, Z., Yu, S., & Hong, C. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104135 · Cited by 39 · see citing works

Condensed Memory Networks for Clinical Diagnostic Inferencing

Prakash, A., Zhao, S., Hasan, S. A., Datla, V. V., Lee, K., Qadir, A., Liu, J., & Farri, O. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10964 · Cited by 39 · see citing works

Active Learning for Biomedical Text Classification Based on Automatically Generated Regular Expressions

Flores, C. A., Figueroa, R. L., & Pezoa, J. E. E. (2021) · IEEE Access · doi:10.1109/access.2021.3064000 · Cited by 37 · see citing works

Automated coding of diagnoses--three methods compared.

Franz, P., Zaiss, A., Schulz, S., Hahn, U., & Klar, R. (2000) · Europe PMC (PubMed Central) · Cited by 31 · see citing works

Multi-channel, convolutional attention based neural model for automated diagnostic coding of unstructured patient discharge summaries

Mayya, V., Kamath, S. S., Krishnan, G. S., & Gangavarapu, T. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.01.013 · Cited by 30 · see citing works

Ontological attention ensembles for capturing semantic concepts in ICD code prediction from clinical text

Falis, M., Pajak, M., Lisowska, A., Schrempf, P., Deckers, L., Mikhael, S. S., Tsaftaris, S. A., & O’Neil, A. Q. (2019) · International Workshop on Health Text Mining and Information Analysis (LOUHI) · doi:10.18653/v1/d19-6220 · Cited by 28 · see citing works

Experimental Evaluation and Development of a Silver-Standard for the MIMIC-III Clinical Coding Dataset

Searle, T., Ibrahim, Z. M., & Dobson, R. (2020) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2020.bionlp-1.8 · Cited by 26 · see citing works

Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

(2018) · doi:10.18653/v1/n18-3 · Cited by 25 · see citing works

A disease inference method based on symptom extraction and bidirectional Long Short Term Memory networks

Guo, D., Duan, G., Yu, Y., Li, Y., Wu, F., & Li, M. (2019) · Methods · doi:10.1016/j.ymeth.2019.07.009 · Cited by 24 · see citing works

Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing

(2020) · doi:10.18653/v1/2020.bionlp-1 · Cited by 23 · see citing works

Data Science for Healthcare: Methodologies and Applications

Consoli, S., Recupero, D. R., & Petković, M. (2019) · TU/e Research Portal · doi:10.1007/978-3-030-05249-2 · Cited by 20 · see citing works

EMR Coding with Semi-Parametric Multi-Head Matching Networks

Rios, A., & Kavuluru, R. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1189 · Cited by 20 · see citing works

Grounded Recurrent Neural NetworksPreprint

Vani, A., Jernite, Y., & Sontag, D. A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.08557 · Cited by 19 · see citing works

Medical code prediction with multi-view convolution and description-regularized label-dependent attentionPreprint

Sadoughi, N., Finley, G. P., Fone, J., Murali, V., Korenevski, M., Baryshnikov, S., Axtmann, N., Miller, M. A., & Suendermann‐Oeft, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.01468 · Cited by 19 · see citing works

Comparative Analysis of Algorithmic Approaches for Auto-Coding with ICD-10-AM and ACHI

Rajvir, K., & Ginige, J. A. (2018) · Studies in health technology and informatics · doi:10.3233/978-1-61499-890-7-73 · Cited by 19 · see citing works

UMLS mapping and Word embeddings for ICD code assignment using the MIMIC-III intensive care database

Schäfer, H., & Friedrich, C. M. (2019) · doi:10.1109/embc.2019.8856442 · Cited by 18 · see citing works

Unsupervised Extraction of Diagnosis Codes from EMRs Using Knowledge-Based and Extractive Text Summarization Techniques

Kavuluru, R., Han, S., & Harris, D. R. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38457-8_7 · Cited by 18 · see citing works

Automated Diagnosis Coding with Combined Text Representations

Berndorfer, S., & Henriksson, A. (2017) · Studies in health technology and informatics · doi:10.3233/978-1-61499-753-5-201 · Cited by 15 · see citing works

Proceedings of the 3rd International Conference on Communication and Information Processing

(2017) · doi:10.1145/3162957 · Cited by 13 · see citing works

Multi-Label Classification of ICD Coding Using Deep Learning

Hsu, C.-C., Chang, P., & Chang, A. (2020) · International Symposium on Community-centric Systems (CcS) · doi:10.1109/ccs49175.2020.9231498 · Cited by 13 · see citing works

Analysing Effectiveness of Multi-Label Classification in Clinical Coding

Kaur, R., & Ginige, J. A. (2019) · Australasian Computer Science Week Multiconference (ACSW) · doi:10.1145/3290688.3290728 · Cited by 9 · see citing works

A comparative analysis of selected set of natural language processing (NLP) and machine learning (ML) algorithms for clinical coding using clinical classification standards

Kaur, R. (2018) · Cited by 7 · see citing works

Distributed Knowledge Based Clinical Auto-Coding System

Kaur, R. (2019) · Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (ACL SRW) · doi:10.18653/v1/p19-2001 · Cited by 6 · see citing works

Scalable Wide and Deep Learning for Computer Assisted Coding

Amoia, M., Diehl, F., Gimenez, J., Pinto, J., Schumann, R., Stemmer, F., Vozila, P., & Zhang, Y. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-3001 · Cited by 4 · see citing works

Automated clinical coding: what, why, and where we are?

Dong, H., Falis, M., Whiteley, W. N., Alex, B., Matterson, J., Ji, S., Chen, J., & Wu, H. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00705-7 · Cited by 129 · see citing works

DRG-LLaMA : tuning LLaMA model to predict diagnosis-related group for hospitalized patients

Wang, H., Gao, C., Dantona, C., Hull, B., & Sun, J. (2024) · npj Digital Medicine · doi:10.1038/s41746-023-00989-3 · Cited by 99 · see citing works

A Unified Review of Deep Learning for Automated Medical Coding

Ji, S., Li, X., Sun, W., Dong, H., Taalas, A., Zhang, Y., Wu, H., Pitkänen, E., & Marttinen, P. (2024) · ACM Computing Surveys · doi:10.1145/3664615 · Cited by 54 · see citing works

Fine-tuning language model embeddings to reveal domain knowledge: An explainable artificial intelligence perspective on medical decision making

Kraišniković, C., Harb, R., Plass, M., Zoughbi, W. A., Holzinger, A., & Müller, H. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109561 · Cited by 53 · see citing works

Systematic evaluation of common natural language processing techniques to codify clinical notes

Tavabi, N., Singh, M., Pruneski, J. A., & Kiapour, A. M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0298892 · Cited by 40 · see citing works

A Scoping Review of Pipeline Maintenance Methodologies Based on Industry 4.0

Naranjo, J. E., Caiza, G. J., Hernández, R. S. V., Castro, M., Alarcón-Ortiz, A., & García, M. V. (2022) · Sustainability · doi:10.3390/su142416723 · Cited by 39 · see citing works

Selective prediction for extracting unstructured clinical data

Swaminathan, A., López, I., Wang, W. Y., Srivastava, U., Tran, E., Bhargava-Shah, A., Wu, J. Y., Ren, A. L., Caoili, K., Bui, B., et al. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad182 · Cited by 27 · see citing works

The application of artificial intelligence on different types of literature reviews - A comparative study

Muller, H., Pachnanda, S., Pahl, F., & Rosenqvist, C. (2022) · International Conference on Applied Artificial Intelligence (ICAPAI) · doi:10.1109/icapai55158.2022.9801564 · Cited by 24 · see citing works

AI integration in nephrology: evaluating ChatGPT for accurate ICD-10 documentation and coding

Abdelgadir, Y. H., Thongprayoon, C., Miao, J., Suppadungsuk, S., Pham, J. H., Mao, M. A., Craici, I. M., & Cheungpasitporn, W. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1457586 · Cited by 19 · see citing works

CARES: A Corpus for classification of Spanish Radiological reports

Chizhikova, M., Úbeda, P. L., Collado-Montañez, J., Martín‐Noguerol, T., Díaz–Galiano, M. C., Luna, A., López, L. A. U., & Valdivia, M. T. M. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106581 · Cited by 18 · see citing works

Image segmentation, classification and recognition methods for comics: A decade systematic literature review

Sharma, R., & Kukreja, V. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107715 · Cited by 18 · see citing works

Common Problems With the Usage of F-Measure and Accuracy Metrics in Medical Research

Lavazza, L., & Morasca, S. (2023) · IEEE Access · doi:10.1109/access.2023.3278996 · Cited by 17 · see citing works

Fine-tuning for accuracy: evaluation of Generative Pretrained Transformer (GPT) for automatic assignment of International Classification of Disease (ICD) codes to clinical documentation

Nawab, K., Fernbach, M., Atreya, S., Asfandiyar, S., Khan, G., Arora, R., Hussain, I., Hijjawi, S. B., & Schreiber, R. (2024) · Journal of Medical Artificial Intelligence · doi:10.21037/jmai-24-60 · Cited by 17 · see citing works

A Systematic Review of NLP Applications in Clinical Healthcare: Advancement and Challenges

Garg, R., & Gupta, A. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-9521-9_3 · Cited by 14 · see citing works

Improving Quality of ICD-10 (International Statistical Classification of Diseases, Tenth Revision) Coding Using AI: Protocol for a Crossover Randomized Controlled Trial

Chomutare, T., Lamproudis, A., Budrionis, A., Svenning, T. O., Hind, L. I., Ngo, P. D., Mikalsen, K. Ø., & Dalianis, H. (2024) · JMIR Research Protocols · doi:10.2196/54593 · Cited by 14 · see citing works

Deep learning for automatic ICD coding: Review, opportunities and challenges

Li, X., Zhang, Y., Hou, X., Wang, S., & Lin, H. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103187 · Cited by 11 · see citing works

Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)

(2022) · doi:10.18653/v1/2022.louhi-1 · Cited by 11 · see citing works

Modelling long medical documents and code associations for explainable automatic ICD coding

Hou, W., Wang, X., Wang, Y., Wang, J., & Xiao, F. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123519 · Cited by 9 · see citing works

How do others cope? Extracting coping strategies for adverse drug events from social media

Dirkson, A. R., Verberne, S., van Oortmerssen, G., Gelderblom, H. A. J., & Kraaij, W. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104228 · Cited by 9 · see citing works

Artificial Intelligence-based Automated International Classification of Diseases Coding: A Systematic Review

Baigi, S. F. M., Sarbaz, M., Darroudi, A., Kemmak, F. D., Aval, R. N., & Kimiafar, K. (2025) · Journal of Medical Signals & Sensors · doi:10.4103/jmss.jmss_76_24 · Cited by 8 · see citing works

TraumaICD Bidirectional Encoder Representation From Transformers

Choi, J., Chen, Y., Sivura, A., Vendrow, E. B., Wang, J., & Spain, D. A. (2023) · Annals of Surgery · doi:10.1097/sla.0000000000006107 · Cited by 8 · see citing works

ChatICD: Prompt Learning for Few-shot ICD Coding through ChatGPT

Liu, J., Yang, S., Peng, T., Hu, X., & Zhu, Q. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385482 · Cited by 8 · see citing works

Divide and Conquer: An Extreme Multi-Label Classification Approach for Coding Diseases and Procedures in Spanish

Barros, J., Rojas, M. S., Dunstan, J., & Abeliuk, A. (2022) · International Workshop on Health Text Mining and Information Analysis (LOUHI) · doi:10.18653/v1/2022.louhi-1.16 · Cited by 8 · see citing works

Automated Generation of ICD-11 Cluster Codes for Precision Medical Record Classification

Feng, J., Zhang, R., Chen, D., Shi, L., & Li, Z. (2024) · International Journal of Computers Communications & Control · doi:10.15837/ijccc.2024.1.6251 · Cited by 7 · see citing works

AnEMIC: A Framework for Benchmarking ICD Coding Models

Kim, J.-Y., Sharma, A., Shanbhogue, S., Weiss, J., & Ravikumar, P. (2022) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2022.emnlp-demos.11 · Cited by 7 · see citing works

VR for construction engineering and management: Review of user experience perspective

Zhang, J., & Jiang, S. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101275 · Cited by 6 · see citing works

Proceedings of TextGraphs-17: Graph-based Methods for Natural Language Processing

(2024) · doi:10.18653/v1/2024.textgraphs-1 · Cited by 6 · see citing works

Preparation for the Implementation of the International Classification of Diseases 11th Revision: A Survey on Clinical Coders' Knowledge of the International Classification of Diseases 11th Revision, Educational Needs, and Readiness to Accept New Roles: A Cross‐Sectional Study

Zarei, J., Hadianfard, A. M., & Sheikhtaheri, A. (2025) · Health Science Reports · doi:10.1002/hsr2.70453 · Cited by 5 · see citing works

Enhancing ICD classification with semantic embedding rectification and long-tail refinement

Wu, Y., Wu, Y., Fan, W., & Li, M. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114530 · Cited by 5 · see citing works

BERT-Driven Natural Language Processing in Electronic Health Records for Improved Clinical Insights

Yadav, K., Sharma, M. K., Michaelson, J., Laxminarayanamma, K., Yadav, V. K., & Krishna, O. (2023) · International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI) · doi:10.1109/icaiihi57871.2023.10489730 · Cited by 5 · see citing works

Natural Language Processing and Machine Learning for Analysis of Radiology Reports: A Systematic Review

Kulsoom, U., Glavin, F. G., & Bendechache, M. (2025) · IEEE Access · doi:10.1109/access.2025.3582728 · Cited by 5 · see citing works

Feature engineering from medical notes: A case study of dementia detection

Miled, Z. B., Dexter, P. R., Grout, R. W., & Boustani, M. A. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e14636 · Cited by 5 · see citing works

Multitask gated interactive network for automatic international classification of diseases coding with dual denoising mechanism

Li, X., Zhang, Y., Hou, X., Wang, S., & Qu, W. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111687 · Cited by 4 · see citing works

Domain-Specific Pretraining of NorDeClin-Bidirectional Encoder Representations From Transformers for International Statistical Classification of Diseases, Tenth Revision, Code Prediction in Norwegian Clinical Texts: Model Development and Evaluation Study

Ngo, P. D., Hernández, M. Á. T., Chomutare, T., Budrionis, A., Svenning, T. O., Torsvik, T., Lamproudis, A., & Dalianis, H. (2025) · JMIR AI · doi:10.2196/66153 · Cited by 4 · see citing works

Using Medical Named Entity Recognition in Automatic ICD Prediction

Kawas, M., Alkhatib, B., Omar, K., Tofelia, K., & Dashash, M. (2025) · BioMed Research International · doi:10.1155/bmri/6117755 · Cited by 3 · see citing works

KAMEL: Knowledge Aware Medical Entity Linkage to Automate Health Insurance Claims Processing

Lui, S. J., Xiang, C., & Krishnaswamy, S. P. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i21.30314 · Cited by 3 · see citing works

EXAMINATION OF SUMMARIZED MEDICAL RECORDS FOR ICD CODE CLASSIFICATION VIA BERT

Kılıç, D. A., Kılıç, D. K., & Nielsen, I. E. (2024) · Applied Computer Science · doi:10.35784/acs-2024-16 · Cited by 3 · see citing works

Extending CARDIO:DE: Additional annotation guidelines and evaluation of NLP approaches for clinical applications

Becker, M., Krumscheid, M., Knobelspies, A., Seydel, M., Richter-Pechanski, P., & Karl, A. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106009 · Cited by 2 · see citing works

Self-supervised Hierarchical Representation for Medication Recommendation

Liang, Y., Liu, Y., Dang, Y., Yang, E., Guo, G., Cai, W., Zhao, J., & Wang, X. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-4155-3_9 · Cited by 2 · see citing works

The role of artificial intelligence in advancing population-based cancer registration

Ding, S., Liu, M., Wang, H., Song, C., Zhao, L., Yang, Z., Wang, Y., Wang, Y., Cui, H., Liu, Z., et al. (2026) · 中国科学通报：英文版 · doi:10.1016/j.scib.2026.02.012 · Cited by 2 · see citing works

Recent Advances in AI for Automated ICD Coding: A Systematic Literature Review

Khalid, A. R., Ali, H., Fathima, K., Carole, K. S., & Kim, H.-C. (2026) · Journal of Medical Systems · doi:10.1007/s10916-026-02429-7 · Cited by 2 · see citing works

Enhancing ICD-9 Code Prediction with BERT: A Multi-Label Classification Approach Using MIMIC-III Clinical Data

Buchh, I. A. (2024) · IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) · doi:10.1109/iotais64014.2024.10799258 · Cited by 2 · see citing works

Enhancing ICD-9 Code Prediction with BERT: A Multi-Label Classification Approach Using MIMIC-III Clinical Data

Buchh, I. A. (2024) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese63988.2024.10912048 · Cited by 2 · see citing works

Using clinical text to refine unspecific condition codes in Dutch general practitioner EHR data

Seinen, T. M., Kors, J. A., van Mulligen, E. M., Fridgeirsson, E. A., Verhamme, K. M. C., & Rijnbeek, P. R. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105506 · Cited by 2 · see citing works

Enhancing ICD-9 Code Prediction with BERT: A Multi-Label Classification Approach Using MIMIC-III Clinical Data

Buchh, I. A. (2024) · IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD) · doi:10.1109/ictmod63116.2024.10878189 · Cited by 1 · see citing works

Machine Learning and Natural Language Processing to Improve Classification of Atrial Septal Defects in Electronic Health Records

Guo, Y., Shi, H., Book, W., Ivey, L. C., Rodriguez, F. H., Sameni, R., Raskind-Hood, C. L., Robichaux, C. J., Downing, K. F., & Sarker, A. (2025) · Birth Defects Research · doi:10.1002/bdr2.2451 · Cited by 1 · see citing works

Improving Rare and Common ICD Coding via a Multi-Agent LLM-Based Approach

Li, R., Wang, X., & Yu, H. Q. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3760894 · Cited by 1 · see citing works

Automated ICD-10–Anchored Classification of Primary Care Text Data: Development and Evaluation of a Custom Multilabel Classifier

Haag, C., Grischott, T., Burgstaller, J. M., Markun, S., Senn, O., & von Wyl, V. (2026) · JMIR Medical Informatics · doi:10.2196/86533 · Cited by 1 · see citing works

A Review of Deep Learning Methods for Automated Clinical Coding

Mahdi, S. S., Deligiannis, N., & Hichem Sahli, B. (2023) · International Conference on Computer and Automation Engineering · doi:10.1109/iccae56788.2023.10111456 · Cited by 1 · see citing works

Towards Understanding Attention-based Reasoning through Graph Structures in Medical Codes Classification

Goldstein, N., Amin, S., & Neumann, G. (2024) · Workshop on Graph Based Methods for Natural Language Processing · doi:10.18653/v1/2024.textgraphs-1.6 · Cited by 1 · see citing works

Deep Learning on Automatic ICD Coding for Clinical Decision Support Systems: Review

Hanifa, A., Permanasari, A. E., & Hidayah, I. (2024) · doi:10.1109/ibiomed62485.2024.10875824 · Cited by 1 · see citing works

HiRes: Hierarchical Feature Optimization and Rescorer for Automatic ICD Coding

Liang, Z., Guan, H., Lü, W., Peng, X., Xu, B. H., & Yang, M. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10887820 · Cited by 1 · see citing works

CATI: A medical context-enhanced framework for diagnosis code assignment in the UK Biobank study

Shen, Y., Wang, J., Wang, Z., Shi, Z., Chen, H., Wang, Z., Jiang, Y., Wang, X., Cheng, C., Wang, X., et al. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103136 · Cited by 1 · see citing works

LLM-assisted clinical coding audit through an interpretable coding pipeline

Khadka, S., Jiang, X., & Palade, V. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106605 · Cited by 1 · see citing works

Large Language Models for Automated Icd-10 Coding of Obstetric Clinical Notes in Portuguese: Comparison With Human CodersPreprint

Santos, R. D. S., Gazzola, M. G., Figueira, P. M., LUZ, A. G., Pacagnella, R. C., & Torezzan, C. (2026) · Research Square · doi:10.21203/rs.3.rs-8712058/v1 · Cited by 1 · see citing works

A Natural Language Processing-Based Turkish Diagnosis Recommendation System

BADEM, S., & KILIÇSAYMAZ, Ö. Ö. (2023) · Bilgisayar Bilimleri ve Teknolojileri Dergisi · doi:10.54047/bibted.1227017

Project Victoria: A pragmatic data model to automate RWE generation from the national French claims database

Touhami, K. O., Ansolabehere, X., Journeau, F., Vidal, A., Jaubourg, N., Doublet, M., Thollot, R., Fabre, A., & Glatt, N. (2025) · Health Informatics Journal · doi:10.1177/14604582251318250

Evaluation of Diagnoses According to ICD‐10 and ICPC‐2 in Family Medicine Practice: A Retrospective Study

Göktaş, O. (2025) · International Journal of Clinical Practice · doi:10.1155/ijcp/7146424

Lymphoma Tumor Coding and Information Extraction: A Comparative Analysis of Large Language Model-Based Methods

Hou, J. L., Fen, Y., Chen, W., Wang, Y., Feng, J., Jin, S. C., Fan, J., Li, J., Gu, J.-C., Lv, S., et al. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-4298-4_9

Comparative study of feature extraction methods for automated ICD code classification using MIMIC-III medical notes and deep learning models

Kılıç, D. A., Kılıç, D. K., & Nielsen, I. E. (2025) · Mathematical Modelling and Numerical Simulation with Applications · doi:10.53391/mmnsa.1666223

Medical Diagnosis Coding Automation: Similarity Search vs. Generative Ai

Klotzman, V. (2024) · Clinical and Medical Engineering Live · doi:10.64030/2997-612x.02.02.01

Unlocking Clinical Narrative Text for Secondary Clinical Usage

AlMutair, L., Atwell, E., & Ravikumar, N. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-9242-2_28

A systematic review of automated International Classification of Diseases coding models using the Medical Information Mart for Intensive Care dataset

Zhang, Y., Lyu, C., Chang, L., Yang, H., Ji, B., & Wei, L.-Y. (2025) · Digital Health · doi:10.1177/20552076251404518

AI in predicting ICD-10 codes and prescriptions medicine for heart failure patients

Noviyani, R., Wijayakusuma, I. G. N. L., Sari, I. A. Y. P., Antara, I. M. P. S., Yowani, S. C., & Widhiartini, I. A. A. (2026) · AIP conference proceedings · doi:10.1063/5.0317732

Automated Clinical Coding with Multiclass ICD Prediction

Khadka, S., Jiang, X., & Palade, V. (2025) · International Conference on Machine Learning and Applications · doi:10.1109/icmla66185.2025.00198

การพฒนาระบบการใหรหสโรคและหตถการอตโนมตสำหรบผปวยนอกโรงพยาบาลสงขลานครนทร: การศกษายอนหลง

ศุภรพิพัฒน์, ส., & ด่านเดชา, พ. (2026) · PSU Medical Journal · doi:10.31584/psumj.2026281538

Patient Journey Modeling Through Multi-Modal Even Fusion for Early ICD Coding

Carnelutti, P.-A., Behzadi-Khormouji, H., Berenguer, A. D., Deligiannis, N., & Hichem Sahli, B. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463054

ICD Coding Automation Model of Retinal Detachment Case Using Support Vector Machine and Random Forest

Kurniawati, D., Nurmalasari, M., Markam, H., & Krismawati, D. (2026) · Eduvest - Journal Of Universal Studies · doi:10.59188/eduvest.v6i5.53072

Labels matter: Incorporating label knowledge into dual branch knowledge distillation framework for long-tailed ICD code assignment

Cai, L., Niu, H., Lv, H., Liu, W., Wang, Z., Liu, Y., & Ren, P. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.104902

Set Prediction for Outpatient Diagnosis Coding with Sparse Mahalanobis Conformal Scoring

Tangudomkit, K., Chairat, S., & Chaichulee, S. (2026) · Big Data and Cognitive Computing · doi:10.3390/bdcc10070232

Machine Learning for Healthcare Revenue Cycle Analytics: A Systematic Review of Claim Denial Prediction, Coding Automation, Payment Delay Forecasting, and Prior Authorization Support

Ricci, P., Luca, M. D., Ferraro, G., & Russo, A. (2024) · Journal of Health Informatics and Digital Systems · doi:10.68159/q291609361

Development and Evaluation of a Metadata-Driven Rule Repository and Daily Screening System for Diagnosis Coding Compliance Under the Korean New DRG Payment System: An Implementation Report (Preprint)

Shin, D., & Hwang, J. (2026) · JMIR Medical Informatics · doi:10.2196/108989

A Systematic Literature Review of Automated ICD Coding and Classification Systems using Discharge Summaries

Kaur, R., Ginige, J. A., & Obst, O. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.10652 · Cited by 17 · see citing works

Low Resource ICD Coding of Hospital Discharge Summaries

Williamson, A., de Hilster, D., Meyers, A., Hubig, N., & Apon, A. (2024) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2024.bionlp-1.45 · Cited by 4 · see citing works

AI-Based Automated ICD Coding for Clinical Texts and Records: A Systematic Literature Review

Sasikala, D., Sabarinath, J., & Sarrvesh, N. (2026) · Springer eBooks · doi:10.1007/978-981-95-4831-6_18

Development and external validation of automated ICD-10 coding from discharge summaries using deep learning approaches

Ponthongmak, W., Thammasudjarit, R., McKay, G. J., Attia, J., Theera-Ampornpunt, N., & Thakkinstian, A. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101227 · Cited by 19 · see citing works

Automated ICD Coding Using Deep Learning

Dhobale, S. S., & Bapat, S. (2021) · Advances in healthcare information systems and administration book series · doi:10.4018/978-1-7998-7709-7.ch007 · Cited by 2 · see citing works