Aprimoramento do diagnóstico automatizado de eletrocardiograma (ECG) por meio de pré-treinamento multimodal com laudos em texto
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1368Palavras-chave:
Aprendizado de Máquina, Eletrocardiografia, CardiologiaResumo
Objective: Heart diseases are the leading cause of death worldwide, and the electrocardiogram (ECG) is the primary diagnostic tool for assessing cardiac activity. Automated and remote ECG diagnosis can help the healthcare system with timely and high-quality cardiac assessments, especially for peripheral regions and rural areas. Automatic ECG classification has been extensively researched, but it is still challenging to build accurate models for such a wide spectrum of scenarios. Method: This study enhances the performance of ECG deep learning classification models using a multimodal pre-training stage with physician's reports. Results: Our approach improves the state-of-the-art model and achieves a mean F1 score of 0.755 over six categories using the full dataset, which is a relevant improvement for a relatively larger unlabeled corpus. Conclusion: The results demonstrate the potential to improve automated cardiac assessment with text pretraining.
Referências
M. Alkmim, A. Ribeiro, G. Carvalho, M. Pena, R. Figueira, and M. Carvalho. Success factors and difficulties for implementation of a telehealth system for remote villages: Minas telecardio project case in brazil. J Health Technol Appl, 5(3):197–202, 2007.
M. B. Alkmim, R. M. Figueira, M. S. Marcolino, C. S. Cardoso, M. P. d. Abreu, L. R. Cunha, D. F. d. Cunha, A. P. Antunes, A. G. d. A. Resende, E. S. Resende, et al. Improving patient access to specialized health care: the telehealth network of minas gerais, brazil. Bulletin of the World Health Organization, 90:373–378, 2012. DOI: https://doi.org/10.2471/BLT.11.099408
S. Bannur, S. Hyland, Q. Liu, F. Perez-Garcia, M. Ilse, D. C. Castro, B. Boeck- ing, H. Sharma, K. Bouzid, A. Thieme, et al. Learning to exploit temporal structure for biomedical vision-language processing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15016–15027, 2023. DOI: https://doi.org/10.1109/CVPR52729.2023.01442
I. Bica, A. Ili ́c, M. Bauer, G. Erdogan, M. Boˇsnjak, C. Kaplanis, A. A. Grit- senko, M. Minderer, C. Blundell, R. Pascanu, et al. Improving fine-grained understanding in image-text pre-training. arXiv preprint arXiv:2401.09865, 2024.
J. Chai, H. Zeng, A. Li, and E. W. Ngai. Deep learning in computer vision: A critical review of emerging techniques and application scenarios. Machine Learning with Applications, 6:100134, 2021. DOI: https://doi.org/10.1016/j.mlwa.2021.100134
Z. Chen, A. H. Cano, A. Romanou, A. Bonnet, K. Matoba, F. Salvi, M. Pagliardini, S. Fan, A. K ̈opf, A. Mohtashami, et al. Meditron-70b: Scaling medical pretraining for large language models. arXiv preprint arXiv:2311.16079, 2023.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
A. Esteva, K. Chou, S. Yeung, N. Naik, A. Madani, A. Mottaghi, Y. Liu, E. Topol, J. Dean, and R. Socher. Deep learning-enabled medical computer vision. NPJ digital medicine, 4(1):5, 2021. DOI: https://doi.org/10.1038/s41746-020-00376-2
K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recogni- tion. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016. DOI: https://doi.org/10.1109/CVPR.2016.90
P. A. Jennett, L. A. Hall, D. Hailey, A. Ohinmaa, C. Anderson, R. Thomas, B. Young, D. Lorenzetti, and R. E. Scott. The socio-economic impact of telehealth: a systematic review. Journal of telemedicine and telecare, 9(6): 311–320, 2003. DOI: https://doi.org/10.1258/135763303771005207
E. M. Lima, A. H. Ribeiro, G. M. Paix ̃ao, M. H. Ribeiro, M. M. Pinto-Filho, P. R. Gomes, D. M. Oliveira, E. C. Sabino, B. B. Duncan, L. Giatti, et al. Deep neural network-estimated electrocardiographic age as a mortality predictor. Nature communications, 12(1):5117, 2021. DOI: https://doi.org/10.1038/s41467-021-25351-7
P. Macfarlane, B. Devine, S. Latif, S. McLaughlin, D. Shoat, and M. Watts. Methodology of ecg interpretation in the glasgow program. Methods of information in medicine, 29(04):354–361, 1990. DOI: https://doi.org/10.1055/s-0038-1634799
P. Macfarlane, B. Devine, and E. Clark. The university of glasgow (uni-g) ecg analysis program. In Computers in Cardiology, 2005, pages 451–454. IEEE, 2005. DOI: https://doi.org/10.1109/CIC.2005.1588134
P. Messina, P. Pino, D. Parra, A. Soto, C. Besa, S. Uribe, M. Andia, C. Tejos, C. Prieto, and D. Capurro. A survey on deep learning and explainability for automatic report generation from medical images. ACM Computing Surveys (CSUR), 54(10s):1–40, 2022. DOI: https://doi.org/10.1145/3522747
M. Moor, Q. Huang, S. Wu, M. Yasunaga, C. Zakka, Y. Dalmia, E. Reis, P. Rajpurkar, and J. Leskovec. Med-flamingo: a multimodal medical few- shot learner (2023). URL: https://arxiv. org/abs/2307.15189, 2023.
D. W. Otter, J. R. Medina, and J. K. Kalita. A survey of the usages of deep learning for natural language processing. IEEE transactions on neural net- works and learning systems, 32(2):604–624, 2020. DOI: https://doi.org/10.1109/TNNLS.2020.2979670
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748–8763. PMLR, 2021.
A. H. Ribeiro, M. H. Ribeiro, G. M. Paix ̃ao, D. M. Oliveira, P. R. Gomes, J. A. Canazart, M. P. Ferreira, C. R. Andersson, P. W. Macfarlane, W. Meira Jr, et al. Automatic diagnosis of the 12-lead ecg using a deep neural network. Nature communications, 11(1):1760, 2020. DOI: https://doi.org/10.1038/s41467-020-15432-4
A. H. Ribeiro, G. Paixao, E. M. Lima, M. H. Ribeiro, M. M. Pinto Filho, P. R. Gomes, D. M. Oliveira, W. Meira Jr, T. B. Schon, and A. L. P. Ribeiro. Code-15%: A large scale annotated dataset of 12-lead ecgs. Zenodo, Jun, 9, 2021.
G. A. Roth, C. Johnson, A. Abajobir, F. Abd-Allah, S. F. Abera, G. Abyu, M. Ahmed, B. Aksut, T. Alam, K. Alam, et al. Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. Journal of the American college of cardiology, 70(1):1–25, 2017.
E. T. R. Schneider, J. V. A. de Souza, J. Knafou, L. E. S. e. Oliveira, J. Co- para, Y. B. Gumiel, L. F. A. d. Oliveira, E. C. Paraiso, D. Teodoro, and C. M. C. M. Barra. BioBERTpt - a Portuguese neural language model for clinical named entity recognition. In Proceedings of the 3rd Clinical Natural Language Processing Workshop, pages 65–72, Online, Nov. 2020. Association for Computational Linguistics. URL https://www.aclweb.org/anthology/ 2020.clinicalnlp-1.7. DOI: https://doi.org/10.18653/v1/2020.clinicalnlp-1.7
S. Wu, K. Roberts, S. Datta, J. Du, Z. Ji, Y. Si, S. Soni, Q. Wang, Q. Wei, Y. Xiang, et al. Deep learning in clinical natural language processing: a methodical review. Journal of the American Medical Informatics Association, 27(3):457–470, 2020. DOI: https://doi.org/10.1093/jamia/ocz200
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