XH-KaaS (eXplanable Health-Knowledge as a Service)
Resumo
Os Sistemas de Suporte à Decisão Clínica (SSDC) e as técnicas de inteligência artificial, especialmente o aprendizado de máquina, tornaram-se verdadeiros aliados devido à sua precisão e eficácia. No entanto, a falta de transparência e interpretabilidade desses sistemas representa desafios para sua aplicação prática. Para mitigar tal problemática, o artigo propõe uma arquitetura de conhecimento como serviço ao domínio da saúde. Essa abordagem busca centralizar serviços e incorporar técnicas de explicabilidade, visando aprimorar a compreensão do processo decisório dos modelos de aprendizado de máquina pelos usuários e estabelecer arquiteturas de referência que minimizem os riscos associados à falta de transparência.
Palavras-chave:
sistemas de suporte à decisão clínica, inteligência artificial, aprendizado de máquina, transparência, interpretabilidade, saúde, explicabilidade, modelos de referência, kaas
Referências
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Barreto, R. (2016). Nefroservice: Plataforma baseada em conhecimento como servic ̧o no dom ́ınio da nefrologia.
Bass, L.; Clements, P. K. R. (2003). Software architecture in practice. Addison-Wesley Professional.
Brunese, L. (2020). Explainable deep learning for pulmonary disease and coronavirus covid-19 detection from x-ray. In Computer Methods and Programs in Biomedicine.
Coroamă, L.; Groza, A. (2022). Evaluation Metrics in Explainable Artificial Intelligence (XAI), pages 401–413.
Doshi-Velez, F.; Kim, B. (2017). Towards a rigorous science of interpretable machine learning.
Greenes, R. A. (2007). Clinical decision support: the road ahead. Elsevier.
Jung, J., Lee, H., Jung, H., and Kim, H. (2023). Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: A systematic review. Heliyon.
McGovern, J., Sims, O., Jain, A., and Little, M. (2006). Enterprise service oriented architectures: Concepts, challenges, recommendations.
Oblizanov, A.; Shevskaya, N., Kazak, A., Rudenko, M., and Dorofeeva, A. (2023). Evaluation metrics research for explainable artificial intelligence global methods using synthetic data. In Appl. Syst. Innov. 2023.
Petkovic, D., Altman, R., Wong, M., and Vigil, A. (2018). Improving the explainability of random forest classifier - user centered approach. In Pacific Symposium on Biocomputing.
Sim, I., Gorman, P., Greenes, R. A., Haynes, R. B., Kaplan, B., Lehmann, H., and Tang, P. C. (2001). Clinical decision support systems for the practice of evidence-based medicine. In J Am Med Inform Assoc.
Speith, T. (2022). A review of taxonomies of explainable artificial intelligence (xai) methods. In ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22).
Xie, B., Lei, T., Wang, N., Cai, H., Xian, J., He, M., Zhang, L., and Xie, H. (2020). Computer-aided diagnosis for fetal brain ultrasound images using deep convolutional neural networks. In International journal of computer assisted radiology and surgery.
Xu, S. and Zhang, W. (2016). Knowledge as a service and knowledge breaching. In 2005 IEEE International Conference on Services Computing (SCC’05).
Barreto, R. (2016). Nefroservice: Plataforma baseada em conhecimento como servic ̧o no dom ́ınio da nefrologia.
Bass, L.; Clements, P. K. R. (2003). Software architecture in practice. Addison-Wesley Professional.
Brunese, L. (2020). Explainable deep learning for pulmonary disease and coronavirus covid-19 detection from x-ray. In Computer Methods and Programs in Biomedicine.
Coroamă, L.; Groza, A. (2022). Evaluation Metrics in Explainable Artificial Intelligence (XAI), pages 401–413.
Doshi-Velez, F.; Kim, B. (2017). Towards a rigorous science of interpretable machine learning.
Greenes, R. A. (2007). Clinical decision support: the road ahead. Elsevier.
Jung, J., Lee, H., Jung, H., and Kim, H. (2023). Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: A systematic review. Heliyon.
McGovern, J., Sims, O., Jain, A., and Little, M. (2006). Enterprise service oriented architectures: Concepts, challenges, recommendations.
Oblizanov, A.; Shevskaya, N., Kazak, A., Rudenko, M., and Dorofeeva, A. (2023). Evaluation metrics research for explainable artificial intelligence global methods using synthetic data. In Appl. Syst. Innov. 2023.
Petkovic, D., Altman, R., Wong, M., and Vigil, A. (2018). Improving the explainability of random forest classifier - user centered approach. In Pacific Symposium on Biocomputing.
Sim, I., Gorman, P., Greenes, R. A., Haynes, R. B., Kaplan, B., Lehmann, H., and Tang, P. C. (2001). Clinical decision support systems for the practice of evidence-based medicine. In J Am Med Inform Assoc.
Speith, T. (2022). A review of taxonomies of explainable artificial intelligence (xai) methods. In ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22).
Xie, B., Lei, T., Wang, N., Cai, H., Xian, J., He, M., Zhang, L., and Xie, H. (2020). Computer-aided diagnosis for fetal brain ultrasound images using deep convolutional neural networks. In International journal of computer assisted radiology and surgery.
Xu, S. and Zhang, W. (2016). Knowledge as a service and knowledge breaching. In 2005 IEEE International Conference on Services Computing (SCC’05).
Publicado
20/05/2024
Como Citar
MONTENEGRO, Thiago C.; LINO, Natasha C. Q..
XH-KaaS (eXplanable Health-Knowledge as a Service). In: TRILHA DE TEMAS, IDEIAS E RESULTADOS EMERGENTES EM SISTEMAS DE INFORMAÇÃO - SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 20. , 2024, Juiz de Fora/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 309-314.
DOI: https://doi.org/10.5753/sbsi_estendido.2024.238984.
