Short Courses of the 25th Brazilian Symposium on Computing Applied to Health

Abou Jaoude, J. and Saade, R. G. (2019). Blockchain applications–usage in different domains. Ieee Access, 7:45360–45381.

Abramitzky, R. e. a. (2021). Automated linking of historical data. Journal of Economic Literature, 59(3):865–918.

Adebayo, J. (2016). Fairml: Toolbox for diagnosing bias in predictive modeling.

Adebukola, A., Navya, A., Jordan, F., Jenifer, N., and Begley, R. D. (2022). Cyber security as a threat to health care. Journal of Technology and Systems, 4(1):32–64.

Adil, M., Khan, M. K., Kumar, N., Attique, M., Farouk, A., Guizani, M., and Jin, Z. (2024). Healthcare internet of things: Security threats, challenges, and future research directions. IEEE Internet of Things Journal, 11(11):19046–19069.

Agarwal, A., Beygelzimer, A., Dudík, M., Langford, J., and Wallach, H. (2018). A reductions approach to fair classification. In Proceedings of the 35th International Conference on Machine Learning, pages 60–69. PMLR.

Ahmed, S. G., Verbert, K., Zaki, N., Khalil, A., Aljassmi, H., and Alnajjar, F. (2025). Ai innovations in rppg systems for driver monitoring: Comprehensive systematic review and future prospects. IEEE Access, 13:22893–22913.

Akkaoui, R., Hei, X., and Cheng, W. (2020). Edgemedichain: A hybrid edge blockchain-based framework for health data exchange. IEEE access, 8:113467–113486.

Alder, S. (2024). Healthcare Data Breaches Due to Phishing. [link].

Aldwean, A. and Tenney, D. (2023). Artificial intelligence in healthcare sector: a literature review of the adoption challenges. Open Journal of Business and Management, 12(1):129–147.

Aljedaani, B. and Babar, M. A. (2021). Challenges With Developing Secure Mobile Health Applications: Systematic Review. JMIR mHealth and uHealth, 9(6):e15654.

Allen, J. (2007). Photoplethysmography and its application in clinical physiological measurement. Physiological measurement, 28(3):R1.

Almeida, L. G. G. et al. (2023) “Innovating Industrial Training with Immersive Metaverses: A Method for Developing Cross-Platform Virtual Reality Environments” Applied Sciences, v. 13, n. 15, p. 8915, 2 ago. 2023.

Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., Yami, M. S. A., Harbi, S. A., and Albekairy, A. M. (2023). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Medical Education, 23(1):689.

Alsentzer, E., Murphy, J., Boag, W., Weng, W.-H., Jindi, D., Naumann, T., and McDermott, M. (2019). Publicly available clinical BERT embeddings. In Rumshisky, A., Roberts, K., Bethard, S., and Naumann, T., editors, Proceedings of the 2nd Clinical Natural Language Processing Workshop, pages 72–78, Minneapolis, Minnesota, USA. Association for Computational Linguistics.

Alves, A. O. (2025) Turismo imersivo em fazendas de cacau: diretrizes para a criação e desenvolvimento de conteúdos em 360º. Dissertação (Mestrado em Modelagem Computacional em Ciência e Tecnologia). Universidade Estadual de Santa Cruz, Ilhéus, 2025.

Aouedi, O., Sacco, A., Piamrat, K., and Marchetto, G. (2023). Handling privacy-sensitive medical data with federated learning: Challenges and future directions. IEEE Journal of Biomedical and Health Informatics, 27(2):790–803.

Aragão, S. M. d. and Schiocchet, T. (2020). Lei Geral de Proteção de Dados: Desafio do Sistema Único de Saúde. Revista Eletrônica de Comunicação, Informação e Inovação em Saúde, 14(3).

Arbabi, M. S., Lal, C., Veeraragavan, N. R., Marijan, D., Nygård, J. F., and Vitenberg, R. (2023). A survey on blockchain for healthcare: Challenges, benefits, and future directions. IEEE Communications Surveys Tutorials, 25(1):386–424.

Arctic Wolf Labs (2025). 2025 Cybersecurity Predictions. [link].

Arellano, A. M., Dai, W., Wang, S., Jiang, X., and Ohno-Machado, L. (2018). Privacy policy and technology in biomedical data science. Annual review of biomedical data science, 1(1):115–129.

Arora, R. and Dell, R. (2023). Linktransformer: A unified package for record linkage with transformer language models.

Associated Press (2020). German hospital hacked, patient taken to another city dies. [link].

Autoridade Nacional de Proteção de Dados (2023). ANPD aplica a primeira multa por descumprimento à LGPD. [link].

Awrahman, B. J., Aziz Fatah, C., and Hamaamin, M. Y. (2022). A review of the role and challenges of big data in healthcare informatics and analytics. Computational intelligence and neuroscience, 2022(1):5317760.

Azuma, R. T. A. (1997) Survey of augmented reality. Presence: Teleoperators and Virtual Environments, v. 6, n. 4, p. 355–385, 1997.

Badawy, M., Ramadan, N., and Hefny, H. A. (2023). Healthcare predictive analytics using machine learning and deep learning techniques: a survey. Journal of Electrical Systems and Information Technology, 10(1):40.

Bao, Y., Chen, Z., Wei, S., Xu, Y., Tang, Z., and Li, H. (2019). The state of the art of data science and engineering in structural health monitoring. Engineering, 5(2):234–242.

Barbosa, B. M., Ribeiro, M. W., Berretta, L, O,, Carvalho, S. T. (2024) “DiagnosTEA: a digital game as a tool for the diagnosis/therapy of Autism Spectrum Disorder” In: Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGAMES) - Trilha Saúde, 2024, Manaus. Anais do SBGAMES 2024. Porto Alegre: SBC, 2024. p. 1707-1718.

Barbosa, G. C. G., Ali, M. S., Araujo, B., Reis, S., Sena, S., Ichihara, M. Y. T., Pescarini, J., Fiaccone, R. L., Amorim, L. D., Pita, R., Barreto, M. E., Smeeth, L., and Barreto, M. L. (2020). Cidacs-rl: a novel indexing search and scoring-based record linkage system for huge datasets with high accuracy and scalability. BMC Medical Informatics and Decision Making, 20(1).

Barocas, S., Hardt, M., and Narayanan, A. (2023). Fairness and machine learning: Limitations and opportunities. MIT press.

Barreto, M. L., Ichihara, M. Y., Almeida, B. A., Barreto, M. E., Cabral, L., Fiaccone, R. L., Carreiro, R. P., Teles, C. A. S., Pitta, R., Penna, G. O., Barral-Netto, M., Ali, M. S., Barbosa, G., Denaxas, S., Rodrigues, L. C., and Smeeth, L. (2019). The centre for data and knowledge integration for health (cidacs): Linking health and social data in brazil. International Journal of Population Data Science, 4(2).

Barreto, M. L., Ichihara, M. Y., Pescarini, J. M., Ali, M. S., Borges, G. L., Fiaccone, R. L., Ribeiro-Silva, R. D. C., Teles, C. A., Almeida, D., Sena, S., Carreiro, R. P., Cabral, L., Almeida, B. A., Barbosa, G. C. G., Pita, R., Barreto, M. E., Mendes, A. A. F., Ramos, D. O., Brickley, E. B., and Smeeth, L. (2022). Cohort profile: The 100 million brazilian cohort. International Journal of Epidemiology, 51(2):E27–E38.

Bazzan, A. L., Tavares, A. R., Pereira, A. G., Jung, C. R., Scharcanski, J., Carbonera, J. L., Lamb, L. C., Recamonde-Mendoza, M., da Silveira, T. L., and Moreira, V. (2023). “A nova eletricidade": Aplicações, riscos e tendências da IA moderna – “The new electricity": Applications, risks, and trends in current AI. arXiv preprint arXiv:2310.18324.

Beckers, K., Heisel, M., and Hatebur, D. (2015). Pattern and security requirements. Pattern Secur. Requir. Eng. Establ. Secur. Stand, pages 1–474.

Beerman, J., Berent, D., Falter, Z., and Bhunia, S. (2023). A review of colonial pipeline ransomware attack. In IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW 2023), pages 8–15.

Bellamy, R. K. E., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilovic, A., Nagar, S., Ramamurthy, K. N., Richards, J., Saha, D., Sattigeri, P., Singh, M., Varshney, K. R., and Zhang, Y. (2018). AI Fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias.

Bencke, L., Paula, F. S., dos Santos, B. G., and Moreira, V. P. (2024). Can we trust llms as relevance judges? In Simpósio Brasileiro de Banco de Dados (SBBD), pages 600–612. SBC.

Bhattacharya, I. and Getoor, L. (2007). Collective entity resolution in relational data. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1):5–es.

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Blake, H., Sharples, L., Harron, K., Van der Meulen, J., and Walker, K. (2022). Linkage of national clinical datasets without patient identifiers using probabilistic methods. International Journal of Population Data Science, 7(3).

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Bobbia, S., Macwan, R., Benezeth, Y., Mansouri, A., and Dubois, J. (2019). Unsupervised skin tissue segmentation for remote photoplethysmography. Pattern Recognition Letters, 124:82–90. Award Winning Papers from the 23rd International Conference on Pattern Recognition (ICPR).

Bogan, R., Gaievski, M., Shah, M., and Kolchina, F. (2025). Introducing reciprocal rank fusion for hybrid search.

Borgese, M., Joyce, C., Anderson, E. E., Churpek, M. M., and Afshar, M. (2022). Bias assessment and correction in machine learning algorithms: a use-case in a natural language processing algorithm to identify hospitalized patients with unhealthy alcohol use. In AMIA Annual Symposium Proceedings, volume 2021, page 247.

Boritz, J. E. (2005). Is practitioners’ views on core concepts of information integrity. International Journal of Accounting Information Systems, 6(4):260–279.

Bradski, G. and Kaehler, A. (2008). Learning OpenCV: Computer vision with the OpenCV library. "O’Reilly Media, Inc.".

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901.

Brunner, U. and Stockinger, K. (2020). Entity matching with transformer architectures-a step forward in data integration. In 23rd International Conference on Extending Database Technology, pages 463–473. OpenProceedings.

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Burkov, A. (2020). Machine Learning Engineering. True Positive Inc.

Burlina, P., Joshi, N., Paul, W., Pacheco, K. D., and Bressler, N. M. (2021). Addressing artificial intelligence bias in retinal diagnostics. Translational Vision Science & Technology, 10(2):13–13.

Calders, T., Kamiran, F., and Pechenizkiy, M. (2009). Building classifiers with independency constraints. In Proceedings of the 2009 IEEE International Conference on Data Mining Workshops, ICDMW ’09, page 13–18, USA. IEEE Computer Society.

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Camargo Jr, K. R. d. and Coeli, C. M. (2015). Going open source: some lessons learned from the development of openreclink. Cadernos De Saude Publica, 31:257–263.

Camargo Júnior, K. and Coeli, C. (2002). Reclink ii: Guia do usuário.

Camargo Junior, K. R. d. and Coeli, C. M. (2006). Reclink 3: nova versão do programa que implementa a técnica de associação probabilística de registros (probabilistic record linkage). Cad. saúde colet.,(Rio J.), pages 399–404.

Camargo, K. and Coeli, C. (2011). Openreclink a free and open source solution for probabilistic record linkage. In AMERICAN JOURNAL OF EPIDEMIOLOGY, volume 173, pages S108–S108. OXFORD UNIV PRESS INC JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA.

Cambricoli, F. (2020). Nova falha do Ministério da Saúde Expõe Dados Pessoais de mais de 200 Milhões de Brasileiros. [link].

Carchiolo, V. and Malgeri, M. (2025). Trends, challenges, and applications of large language models in healthcare: A bibliometric and scoping review. Future Internet, 17(2):76.

Cartwright, A. J. (2023). The Elephant in the Room: Cybersecurity in Healthcare. Journal of Clinical Monitoring and Computing, 37(5):1123–1132.

Caton, S. and Haas, C. (2024). Fairness in machine learning: A survey. ACM Computing Surveys, 56(7):1–38.

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Chamberlaim, A., de Azevedo Flor, B., da Silva Pereira, E., Almeida, L. S., Martins, L. D., Silva, Y. S., Siqueira, G. G., Maiczak, T., and Bovo, F. (2023). Inteligência artificial (ia) e suas aplicações em exames de imagem: uma nova era para diagnósticos na área da saúde. Cuadernos de Educación y Desarrollo, 15(12):17605–17624.

Chattu, V. K. (2021). A review of artificial intelligence, big data, and blockchain technology applications in medicine and global health. Big Data and Cognitive Computing, 5(3):41.

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Chen, I. Y., Pierson, E., Rose, S., Joshi, S., Ferryman, K., and Ghassemi, M. (2021). Ethical machine learning in healthcare. Annual Review of Biomedical Data Science Annu. Rev. Biomed. Data Sci, 2021:123–144.

Chen, W. and McDuff, D. (2018). Deepphys: Video-based physiological measurement using convolutional attention networks. pages 356–373.

Chowdhury, M., He, Y. V., Higham, A., and Lim, E. (2025). Astrid–an automated and scalable triad for the evaluation of rag-based clinical question answering systems. arXiv preprint arXiv:2501.08208.

Christen, P. (2011). A survey of indexing techniques for scalable record linkage and deduplication. IEEE transactions on knowledge and data engineering, 24(9):1537–1555.

Christen, P. (2012). Data matching: Concepts and techniques for record linkage, entity resolution, and duplicate detection. Springer Science & Business Media.

Christen, P. (2014). Privacy aspects in big data integration: Challenges and opportunities. In Proceedings of the First International Workshop on Privacy and Secuirty of Big Data, PSBD ’14, page 1, New York, NY, USA. Association for Computing Machinery.

Christen, P. (2019). Data linkage: The big picture. Harvard Data Science Review, 1(2).

Christen, P., Ranbaduge, T., and Schnell, R. (2020). Linking Sensitive Data: Methods and Techniques for Practical Privacy-Preserving Information Sharing. Springer Cham.

Christen, P., Schnell, R., and Vidanage, A. (2025). Information leakage in data linkage. arXiv preprint arXiv:2505.08596.

Christen, P., Ziyad, S., Vidanage, A., Nanayakkara, C., and Schnell, R. (2024). A single parameter method for secure privacy preserving record linkage. International Journal of Population Data Science, 9(5).

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Coeli, C. M. e. a. (2021). Record linkage under suboptimal conditions for data-intensive evaluation of primary care in rio de janeiro, brazil. BMC Medical Informatics and Decision Making, 21(1):190.

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Colacci, M., Huang, Y. Q., Postill, G., Zhelnov, P., Fennelly, O., Verma, A., Straus, S., and Tricco, A. C. (2024). Sociodemographic bias in clinical machine learning models: a scoping review of algorithmic bias instances and mechanisms. Journal of Clinical Epidemiology, page 111606.

Collins, F. S. and Varmus, H. (2015). A new initiative on precision medicine. New England journal of medicine, 372(9):793–795.

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Cordeiro, A., Santos, F., Winkler, I. (2023) “Effectiveness of industrial training using virtual reality to mitigate risks associated with the work environment: a literature review” International Symposium on Innovation and Technology, Engineering and the Future of the Industry, 2023. ISSN: 2357-759.

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