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Mapping a Clinical Case Description to an Argumentation Framework: A Preliminary Assessment

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Intelligent Data Engineering and Automated Learning – IDEAL 2020 (IDEAL 2020)

Abstract

Medical reasoning in the context of multiple co-existing diseases poses challenges to healthcare professionals by demanding a careful consideration of possible harmful interactions. Computational argumentation, with its conflict resolution capabilities, may assist medical decisions by sorting out these interactions. Unfortunately, most of the argumentation work developed for medical reasoning has not been widely applied to real clinical sources. In this work, we select ASPIC+G and formalise a real clinical case according to the definitions of this argumentation framework. We found limitations in the representation of a patient’s evolution and the formalisation of clinical rules which can be inferred from the context of the clinical case.

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Notes

  1. 1.

    s.t.: such that.

  2. 2.

    Here we omit the second index.

References

  1. Costa, R., Neves, J., Novais, P., Machado, J., Lima, L., Alberto, C.: Intelligent mixed reality for the creation of ambient assisted living. In: Neves, J., Santos, M.F., Machado, J.M. (eds.) EPIA 2007. LNCS (LNAI), vol. 4874, pp. 323–331. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-77002-2_27

    Chapter  Google Scholar 

  2. Cook, D.A., Sherbino, J., Durning, S.J.: Management reasoning: beyond the diagnosis. Jama 319(22), 2267–2268 (2018)

    Article  Google Scholar 

  3. Fraccaro, P., Casteleiro, M.A., Ainsworth, J., Buchan, I.: Adoption of clinical decision support in multimorbidity: a systematic review. JMIR Med. Inform. 3(1), e4 (2015)

    Article  Google Scholar 

  4. Sutton, D.R., Fox, J.: The syntax and semantics of the pro forma guideline modeling language. J. Am. Med. Inform. Assoc. 10(5), 433–443 (2003)

    Article  Google Scholar 

  5. Oliveira, T., Dauphin, J., Satoh, K., Tsumoto, S., Novais, P.: Goal-driven structured argumentation for patient management in a multimorbidity setting. In: Dastani, M., Dong, H., van der Torre, L. (eds.) CLAR 2020. LNCS (LNAI), vol. 12061, pp. 166–183. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44638-3_11

    Chapter  Google Scholar 

  6. Johnson, A.E.W., et al.: Mimic-iii, a freely accessible critical care database. Sci. Data 3(1), 160035 (2016). https://doi.org/10.1038/sdata.2016.35

  7. Grando, M.A., Glasspool, D., Boxwala, A.: Argumentation logic for the flexible enactment of goal-based medical guidelines. J. Biomed. Inform. 45(5), 938–949 (2012)

    Article  Google Scholar 

  8. Hunter, A., Williams, M.: Aggregating evidence about the positive and negative effects of treatments. Artif. Intell. Med. 56(3), 173–190 (2012)

    Article  Google Scholar 

  9. Kokciyan, N., et al.: Towards an argumentation system for supporting patients in self-managing their chronic conditions. In: Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  10. Modgil, S., Prakken, H.: The ASPIC+ framework for structured argumentation: a tutorial. Argument Comput. 5, 31–62 (2014)

    Article  Google Scholar 

  11. Carneiro, D., Novais, P., Pêgo, J.M., Sousa, N., Neves, J.: Using mouse dynamics to assess stress during online exams. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds.) HAIS 2015. LNCS (LNAI), vol. 9121, pp. 345–356. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19644-2_29

    Chapter  Google Scholar 

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Acknowledgments

This work has been supported by FCT - Fundação para a Ciência e a Tecnologia within the R&D Units project scope UIDB/00319/2020. The work of Ana Silva is also supported by a Portuguese doctoral grant, SFRH/BD/143512/2019, issued by FCT in Portugal.

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Silva, A., Silva, A., Oliveira, T., Novais, P. (2020). Mapping a Clinical Case Description to an Argumentation Framework: A Preliminary Assessment. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham. https://doi.org/10.1007/978-3-030-62362-3_30

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  • DOI: https://doi.org/10.1007/978-3-030-62362-3_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62361-6

  • Online ISBN: 978-3-030-62362-3

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