Avaliação de modelos para extração de dados não estruturados de um sistema EHR para atender a estrutura final de uma ontologia

• Diego Pinheiro da Silva UNISINOS
• Blanda Helena de Mello UNISINOS
• William da Rosa Fröhlich UNISINOS
• Sandro José Rigo UNISINOS
• Marco Antonio Schwertner UNISINOS
• Cristiano André da Costa UNISINOS

DOI: https://doi.org/10.5753/sbcas.2022.222725

Abstract

There is a significant increase in the number of Electronic Health Records (EHRs) that accommodate unstructured data in natural language. Manual analysis of this data is not feasible due to the large volume, whose tendency is to continue to increase. In this scenario, there is a need for approaches that allow the structuring of this information to help health professionals in data analysis, treatment indication, disease diagnosis, and others. This research aims to develop a model for processing unstructured data from EHRs, observing the objective of representing them in ontology structures. Preliminary experiments were carried out and indicated promising results for the model.

References

Adlung, L. et al. (2021). Machine learning in clinical decision making. Med.

Alemzadeh, H. and Devarakonda, M. (2017). An nlp-based cognitive system for disease status identification in electronic health records. In 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pages 89-92. IEEE.

Bucur, A. et al. (2013). Clinical decision support framework for validation of multiscale models and personalization of treatment in oncology. In 13th IEEE International Conference on BioInformatics and BioEngineering, pages 1-4. IEEE.

Christopoulou, F. et al. (2020). Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods. Journal of the American Medical Informatics Association, 27(1):39-46.

Cuocolo, R. et al. (2020). Machine learning in oncology: a clinical appraisal. Cancer letters, 481:55-62.

de Souza, J. V. A. et al. (2021). A multilabel approach to portuguese clinical named entity recognition. Journal of Health Informatics, 12.

Devlin, J. et al. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

Dhole, G. and Uke, N. (2014). Nlp based retrieval of medical information for diagnosis of human diseases. Int J Renew Energy Technol, 3(10):243e8.

Ford, E. et al. (2016). Extracting information from the text of electronic medical records to improve case detection: a systematic review. Journal of the American Medical Informatics Association, 23(5):1007-1015.

Gonzalez-Hernandez, G. et al. (2017). Capturing the patient's perspective: a review of advances in natural language processing of health-related text. Yearbook of medical informatics, 26(1):214.

Ji, Z. et al. (2020). Bert-based ranking for biomedical entity normalization. AMIA Summits on Translational Science Proceedings, 2020:269.

Kitchenham, B. A. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical Report EBSE 2007-001, Keele University and Durham University Joint Report.

Koleck, T. A. et al. (2019). Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review. Journal of the American Medical Informatics Association, 26(4):364-379.

Kreimeyer, K. et al. (2017). Natural language processing systems for capturing and standardizing unstructured clinical information: a systematic review. Journal of biomedical informatics, 73:14-29.

Lee, H. et al. (2016). Quote recommendation in dialogue using deep neural network. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pages 957-960.

Li, Y. et al. (2020). Behrt: transformer for electronic health records. 10(1):1-12.

Lopes, É. et al. (2021). Exploring bert for aspect extraction in portuguese language. In The International FLAIRS Conference Proceedings, volume 34.

Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 9:381-386.

Morgan, S. et al. (2021). Wlv-rit at germeval 2021: Multitask learning with transformers to detect toxic, engaging, and fact-claiming comments. arXiv preprint arXiv:2108.00057.

Ngiam, K. Y. and Khor, W. (2019). Big data and machine learning algorithms for healthcare delivery. The Lancet Oncology, 20(5):e262-e273.

Prisma (2021). Preferred reporting items for systematic reviews and meta-analyses. Disponível em: <http://prisma-statement.org/PRISMAStatement/Checklist.aspx>. Acessado em 08/04/2021.

Schneider, E. T. R. et al. (2020). Biobertpt-a portuguese neural language model for clinical named entity recognition. In Proceedings of the 3rd Clinical Natural Language Processing Workshop, pages 65-72.

Segura Bedmar, I. et al. (2013). Semeval-2013 task 9: Extraction of drug-drug interactions from biomedical texts (ddiextraction 2013). Association for Computational Linguistics.

Souza, F. et al. (2020). Bertimbau: pretrained bert models for brazilian portuguese. In Brazilian Conference on Intelligent Systems, pages 403-417. Springer.

Xue, K. et al. (2019). Fine-tuning bert for joint entity and relation extraction in chinese medical text. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 892-897. IEEE.

Yang, X. et al. (2019). Madex: a system for detecting medications, adverse drug events, and their relations from clinical notes. Drug safety, 42(1):123-133.