Integração de Sensoriamento e Predição de Risco Clínico em Sistema IoMT
Resumo
As doenças cardiovasculares permanecem como a principal causa de morte no mundo, especialmente em países de baixa e média renda, onde o diagnóstico precoce depende de avaliações presenciais. Nesse contexto, sistemas de telemonitoramento podem apoiar o acompanhamento de sinais vitais fora do ambiente hospitalar quando combinam medições confiáveis e análise automática de risco. Este trabalho apresenta um sistema IoMT que integra sensoriamento calibrado e predição de risco clínico em uma arquitetura Edge-Fog-Cloud. O protótipo utiliza um ESP32 com sensores de temperatura, frequência cardíaca e SpO2. As leituras são agregadas por um serviço Fog, que aciona modelos treinados sobre o MIMIC-III com base em uma versão simplificada do NEWS2 e mantém operação local por meio de fallback. O sistema emprega MQTT e visualização quase em tempo real, indicando a viabilidade de integrar caracterização metrológica e inferência preditiva em um fluxo contínuo de telemonitoramento.
Referências
Al Farizi, W. S., Hidayah, I., and Rizal, M. N. (2021). Isolation forest based anomaly detection: A systematic literature review. In 2021 8th International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE), pages 118–122.
Bezerra, T., Vinícius, M., Wallafy, F., Bezerra, F., França, C., and Callou, G. (2025a). Uma abordagem iot com software livre para o monitoramento inteligente e controle ambiental na saúde. In Congresso Latino-Americano de Software Livre e Tecnologias Abertas (Latinoware), pages 581–590. SBC.
Bezerra, T. V., Callou, G., Airton, F., and Tavares, E. (2025b). A modeling-based approach for performance and availability assessment of iomt systems. Electronics, 14(6):1157.
Breiman, L. (2001). Random forests. Machine Learning, 45(1):5–32.
Chandani, V., Kotnis, A., and Kolhare, N. R. (2025). Affordable heart health monitoring system using esp32, ad8232 and max30102. COEP Technological University, Pune, India.
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16:321–357.
Chen, T. and Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16), pages 785–794, San Francisco, CA, USA.
He, H., Bai, Y., Garcia, E. A., and Li, S. (2008). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pages 1322–1328, Hong Kong, China.
Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-w. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., and Mark, R. G. (2016). Mimic-iii, a freely accessible critical care database. Scientific Data, 3:160035.
Locharoenrat, K. (2026). Iomt–fog–cloud-based ai frameworks for chronic disease diagnosis: updated comparative analysis with recent ai-iomt models (2020–2025). Frontiers in Medical Technology.
Moreira, F. (2025). A hybrid fog-edge computing architecture for real-time health monitoring in iomt systems with optimized latency and threat resilience. Scientific Reports, 15(1):25655.
Patel, H., Crusco, S., Hansen, D., Morales, J., Patale, T., Chiuzan, C., Sands, M., Naidich, J., Myers, K., Davidson, K. W., Hirsch, J. S., and Zanos, T. P. (2026). A systematic review and meta-analysis of externally validated epic clinical decision support tools. Journal of General Internal Medicine.
Royal College of Physicians (2017). National early warning score (news) 2: Standardising the assessment of acute-illness severity in the nhs. updated report of a working party. Technical report, Royal College of Physicians, London.
Scheid, M. R., Friedmann, B., Oppenheim, M., Hirsch, J. S., and Zanos, T. P. (2025). Development and validation of a clinical wearable deep learning based continuous inhospital deterioration prediction model. Nature Communications, 16:9513.
Shashikumar, S. P., Le, J. P., Yung, N., Ford, J., Singh, K., Malhotra, A., Nemati, S., and Wardi, G. (2024). Development and validation of a deep learning model for prediction of adult physiological deterioration. Critical Care Explorations, 6(9):e1151.
Steitz, B. D., McCoy, A. B., Reese, T. J., Liu, S., Weavind, L., Shipley, K., Russo, E., and Wright, A. (2023). Development and validation of a machine learning algorithm using clinical pages to predict imminent clinical deterioration. Journal of General Internal Medicine, 39(1):27–35.
Valentim, T., Callou, G., França, C., and Tavares, E. (2024). Availability and performance assessment of iomt systems: a stochastic modeling approach. Journal of Network and Systems Management, 32(4):95.
World Health Organization (2025). Cardiovascular diseases (cvds). Fact sheet, atualizado em 31 de julho de 2025.
Yulizar, D., Soekirno, S., Ananda, N., Prabowo, M. A., Perdana, I. F. P., and Aofany, D. (2023). Performance analysis comparison of dht11, dht22 and ds18b20 as temperature measurement. In Proceedings of ICSES 2022, pages 37–45.
Bezerra, T., Vinícius, M., Wallafy, F., Bezerra, F., França, C., and Callou, G. (2025a). Uma abordagem iot com software livre para o monitoramento inteligente e controle ambiental na saúde. In Congresso Latino-Americano de Software Livre e Tecnologias Abertas (Latinoware), pages 581–590. SBC.
Bezerra, T. V., Callou, G., Airton, F., and Tavares, E. (2025b). A modeling-based approach for performance and availability assessment of iomt systems. Electronics, 14(6):1157.
Breiman, L. (2001). Random forests. Machine Learning, 45(1):5–32.
Chandani, V., Kotnis, A., and Kolhare, N. R. (2025). Affordable heart health monitoring system using esp32, ad8232 and max30102. COEP Technological University, Pune, India.
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16:321–357.
Chen, T. and Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16), pages 785–794, San Francisco, CA, USA.
He, H., Bai, Y., Garcia, E. A., and Li, S. (2008). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pages 1322–1328, Hong Kong, China.
Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-w. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., and Mark, R. G. (2016). Mimic-iii, a freely accessible critical care database. Scientific Data, 3:160035.
Locharoenrat, K. (2026). Iomt–fog–cloud-based ai frameworks for chronic disease diagnosis: updated comparative analysis with recent ai-iomt models (2020–2025). Frontiers in Medical Technology.
Moreira, F. (2025). A hybrid fog-edge computing architecture for real-time health monitoring in iomt systems with optimized latency and threat resilience. Scientific Reports, 15(1):25655.
Patel, H., Crusco, S., Hansen, D., Morales, J., Patale, T., Chiuzan, C., Sands, M., Naidich, J., Myers, K., Davidson, K. W., Hirsch, J. S., and Zanos, T. P. (2026). A systematic review and meta-analysis of externally validated epic clinical decision support tools. Journal of General Internal Medicine.
Royal College of Physicians (2017). National early warning score (news) 2: Standardising the assessment of acute-illness severity in the nhs. updated report of a working party. Technical report, Royal College of Physicians, London.
Scheid, M. R., Friedmann, B., Oppenheim, M., Hirsch, J. S., and Zanos, T. P. (2025). Development and validation of a clinical wearable deep learning based continuous inhospital deterioration prediction model. Nature Communications, 16:9513.
Shashikumar, S. P., Le, J. P., Yung, N., Ford, J., Singh, K., Malhotra, A., Nemati, S., and Wardi, G. (2024). Development and validation of a deep learning model for prediction of adult physiological deterioration. Critical Care Explorations, 6(9):e1151.
Steitz, B. D., McCoy, A. B., Reese, T. J., Liu, S., Weavind, L., Shipley, K., Russo, E., and Wright, A. (2023). Development and validation of a machine learning algorithm using clinical pages to predict imminent clinical deterioration. Journal of General Internal Medicine, 39(1):27–35.
Valentim, T., Callou, G., França, C., and Tavares, E. (2024). Availability and performance assessment of iomt systems: a stochastic modeling approach. Journal of Network and Systems Management, 32(4):95.
World Health Organization (2025). Cardiovascular diseases (cvds). Fact sheet, atualizado em 31 de julho de 2025.
Yulizar, D., Soekirno, S., Ananda, N., Prabowo, M. A., Perdana, I. F. P., and Aofany, D. (2023). Performance analysis comparison of dht11, dht22 and ds18b20 as temperature measurement. In Proceedings of ICSES 2022, pages 37–45.
Publicado
23/09/2026
Como Citar
LEÃO, Arianny; SILVA, Luiz; LINS, Lucas; LIMA, Italo; LOPES, Diogo; BEZERRA, Thiago.
Integração de Sensoriamento e Predição de Risco Clínico em Sistema IoMT. In: ESCOLA REGIONAL DE COMPUTAÇÃO BAHIA, ALAGOAS E SERGIPE (ERBASE), 26. , 2026, Maragogi/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 299-308.
DOI: https://doi.org/10.5753/erbase.2026.32236.
