Clustering Corticosteroids Responsiveness in Sepsis Patients using Game-Theoretic Rough Sets

• Autorzy: Hellali Rahma , Dagdia Zaineb Chelly , Zeitouni Karine

Identyfikatory

DOI

10.15439/2023F9521

• Języki publikacji: EN

Abstrakty

EN

Performing data mining tasks in the medical domain poses a significant challenge, mainly due to the uncertainty present in patients' data, such as incompleteness or missingness. In this paper, we focus on the data mining task of clustering corticosteroid (CS) responsiveness in sepsis patients. We address the issue and challenge of missing data by applying Game-Theoretic Rough Sets (GTRS) as a three-way decision approach. Our study considers the APROCCHS cohort, comprising 1240 sepsis patients, provided by the Assistance Publique--Hôpitaux de Paris (AP-HP), France. Our experimental results on the APROCCHS cohort indicate that GTRS maintains the trade-off between accuracy and generality, demonstrating its effectiveness even when increasing the number of missing values.

Słowa kluczowe

EN

computer science; uncertainty; rough set; sepsis; data mining; task analysis

PL

informatyka; niepewność; zbiór przybliżony; sepsa; eksploracja danych; analiza zadań

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 545--556
• Opis fizyczny: Bibliogr. 43 poz., il., wz., tab.

Twórcy

autor

Hellali Rahma

• Université Paris-Saclay, UVSQ, DAVID, France

autor

Dagdia Zaineb Chelly

• Université Paris-Saclay, UVSQ, DAVID, France Université de Tunis
• Institut Supérieur de Gestion de Tunis, LARODEC, Tunisia

autor

Zeitouni Karine

• Université Paris-Saclay, UVSQ, DAVID, France

Bibliografia

• 1. M. Singer, C. S. Deutschman, C. W. Seymour, M. Shankar-Hari, D. Annane, M. Bauer, R. Bellomo, G. R. Bernard, J.-D. Chiche, C. M. Coopersmith et al., “The third international consensus definitions for sepsis and septic shock (sepsis-3),” Jama, vol. 315, no. 8, pp. 801–810, 2016.
• 2. J. Matsuda, S. Kato, H. Yano, G. Nitta, T. Kono, T. Ikenouchi, K. Murata, M. Kanoh, Y. Inamura, T. Takamiya et al., “The sequential organ failure assessment (sofa) score predicts mortality and neurological outcome in patients with post-cardiac arrest syndrome,” Journal of cardiology, vol. 76, no. 3, pp. 295–302, 2020.
• 3. K. E. Rudd, S. C. Johnson, K. M. Agesa, K. A. Shackelford, D. Tsoi, D. R. Kievlan, D. V. Colombara, K. S. Ikuta, N. Kissoon, S. Finfer et al., “Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the global burden of disease study,” The Lancet, vol. 395, no. 10219, pp. 200–211, 2020.
• 4. A. Rhodes, L. E. Evans, W. Alhazzani, M. M. Levy, M. Antonelli, R. Ferrer, A. Kumar, J. E. Sevransky, C. L. Sprung, M. E. Nunnally et al., “Surviving sepsis campaign: international guidelines for management of sepsis and septic shock: 2016,” Intensive care medicine, vol. 43, no. 3, pp. 304–377, 2017.
• 5. D. W. Cain and J. A. Cidlowski, “Immune regulation by glucocorticoids,” Nature Reviews Immunology, vol. 17, no. 4, pp. 233–247, 2017.
• 6. D. Annane, S. M. Pastores, W. Arlt, R. A. Balk, A. Beishuizen, J. Briegel, J. Carcillo, M. Christ-Crain, M. S. Cooper, P. E. Marik et al., “Critical illness-related corticosteroid insufficiency (circi): a narrative review from a multispecialty task force of the society of critical care medicine (sccm) and the european society of intensive care medicine (esicm),” Intensive care medicine, vol. 43, no. 12, pp. 1781–1792, 2017.
• 7. N. Heming, S. Sivanandamoorthy, P. Meng, R. Bounab, and D. Annane, “Immune effects of corticosteroids in sepsis,” Frontiers in Immunology, p. 1736, 2018.
• 8. D. Annane, “Corticosteroids for severe sepsis: an evidence-based guide for physicians,” Annals of intensive care, vol. 1, no. 1, pp. 1–7, 2011.
• 9. J. Cleve and U. Lämmel, Data mining. Walter de Gruyter GmbH & Co KG, 2020.
• 10. S. Gavankar and S. Sawarkar, “Decision tree: Review of techniques for missing values at training, testing and compatibility,” in 2015 3rd international conference on artificial intelligence, modelling and simulation (AIMS). IEEE, 2015, pp. 122–126.
• 11. S. A. Alasadi and W. S. Bhaya, “Review of data preprocessing techniques in data mining,” Journal of Engineering and Applied Sciences, vol. 12, no. 16, pp. 4102–4107, 2017.
• 12. L. Yu, R. Zhou, R. Chen, and K. K. Lai, “Missing data preprocessing in credit classification: One-hot encoding or imputation?” Emerging Markets Finance and Trade, vol. 58, no. 2, pp. 472–482, 2022.
• 13. M. S. Osman, A. M. Abu-Mahfouz, and P. R. Page, “A survey on data imputation techniques: Water distribution system as a use case,” IEEE Access, vol. 6, pp. 63 279–63 291, 2018.
• 14. R. R. Andridge and R. J. Little, “A review of hot deck imputation for survey non-response,” International statistical review, vol. 78, no. 1, pp. 40–64, 2010.
• 15. U. Pujianto, A. P. Wibawa, M. I. Akbar et al., “K-nearest neighbor (k-nn) based missing data imputation,” in 2019 5th International Conference on Science in Information Technology (ICSITech). IEEE, 2019, pp. 83–88.
• 16. N. Karmitsa, S. Taheri, A. Bagirov, and P. Mäkinen, “Missing value imputation via clusterwise linear regression,” IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 4, pp. 1889–1901, 2020.
• 17. R. A. Hughes, J. Heron, J. A. Sterne, and K. Tilling, “Accounting for missing data in statistical analyses: multiple imputation is not always the answer,” International journal of epidemiology, vol. 48, no. 4, pp. 1294–1304, 2019.
• 18. S. Goel and M. Tushir, “Different approaches for missing data handling in fuzzy clustering: a review,” Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering), vol. 13, no. 6, pp. 833–846, 2020.
• 19. D.-T. Dinh, V.-N. Huynh, and S. Sriboonchitta, “Clustering mixed numerical and categorical data with missing values,” Information Sciences, vol. 571, pp. 418–442, 2021.
• 20. Z. Pawlak, “Rough sets,” International journal of computer & information sciences, vol. 11, pp. 341–356, 1982.
• 21. A. Skowron and D. Ślezak, “Rough sets turn 40: From information systems to intelligent systems,” in 2022 17th Conference on Computer Science and Intelligence Systems (FedCSIS). IEEE, 2022, pp. 23–34.
• 22. T.-F. Fan, C.-J. Liau, and D.-R. Liu, “Variable precision fuzzy rough set based on relative cardinality,” in 2012 Federated Conference on Computer Science and Information Systems (FedCSIS). IEEE, 2012, pp. 43–47.
• 23. L. S. Riza, A. Janusz, C. Bergmeir, C. Cornelis, F. Herrera, D. Śle, J. M. Benı́tez et al., “Implementing algorithms of rough set theory and fuzzy rough set theory in the r package “roughsets”,” Information sciences, vol. 287, pp. 68–89, 2014.
• 24. R. Polikar, J. DePasquale, H. S. Mohammed, G. Brown, and L. I. Kuncheva, “Learn++. mf: A random subspace approach for the missing feature problem,” Pattern Recognition, vol. 43, no. 11, pp. 3817–3832, 2010.
• 25. B. Panda, S. Gantayat, and A. Misra, “Rough set rule-based technique for the retrieval of missing data in malaria diseases diagnosis,” Computational Intelligence in Medical Informatics, pp. 59–71, 2015.
• 26. P. Maji, “Advances in rough set based hybrid approaches for medical image analysis,” in Rough Sets: International Joint Conference, IJCRS 2017, Olsztyn, Poland, July 3–7, 2017, Proceedings, Part I. Springer, 2017, pp. 25–33.
• 27. K. B. Nahato, K. N. Harichandran, K. Arputharaj et al., “Knowledge mining from clinical datasets using rough sets and backpropagation neural network,” Computational and mathematical methods in medicine, vol. 2015, 2015.
• 28. Y. Yao et al., “An outline of a theory of three-way decisions.” in RSCTC, vol. 7413, 2012, pp. 1–17.
• 29. M. K. Afridi, N. Azam, J. Yao, and E. Alanazi, “A three-way clustering approach for handling missing data using gtrs,” International Journal of Approximate Reasoning, vol. 98, pp. 11–24, 2018.
• 30. C. M. Poteraş and M. L. Mocanu, “Evaluation of an optimized k-means algorithm based on real data,” in 2016 Federated Conference on Computer Science and Information Systems (FedCSIS). IEEE, 2016, pp. 831–835.
• 31. J. P. Herbert and J. Yao, “Game-theoretic rough sets,” Fundamenta Informaticae, vol. 108, no. 3-4, pp. 267–286, 2011.
• 32. J. Yao and J. P. Herbert, “A game-theoretic perspective on rough set analysis,” Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), vol. 20, no. 3, pp. 291–298, 2008.
• 33. N. Azam and J. Yao, “Analyzing uncertainties of probabilistic rough set regions with game-theoretic rough sets,” International journal of approximate reasoning, vol. 55, no. 1, pp. 142–155, 2014.
• 34. Y. Shoham, “Computer science and game theory,” Communications of the ACM, vol. 51, no. 8, pp. 74–79, 2008.
• 35. K. Leyton-Brown and Y. Shoham, Essentials of game theory: A concise multidisciplinary introduction. Springer Nature, 2022.
• 36. R. S. Hotchkiss, E. Colston, S. Yende, D. C. Angus, L. L. Moldawer, E. D. Crouser, G. S. Martin, C. M. Coopersmith, S. Brakenridge, F. B. Mayr et al., “Immune checkpoint inhibition in sepsis: a phase 1b randomized, placebo-controlled, single ascending dose study of anti-pd-l1 (bms-936559),” Critical care medicine, vol. 47, no. 5, p. 632, 2019.
• 37. T. Z. J. Teng, J. K. T. Tan, S. Baey, S. K. Gunasekaran, S. P. Junnarkar, J. K. Low, C. W. T. Huey, and V. G. Shelat, “Sequential organ failure assessment score is superior to other prognostic indices in acute pancreatitis,” World Journal of Critical Care Medicine, vol. 10, no. 6, p. 355, 2021.
• 38. I. Oz and S. Arslan, “A survey on multithreading alternatives for soft error fault tolerance,” ACM Computing Surveys (CSUR), vol. 52, no. 2, pp. 1–38, 2019.
• 39. M. Steinegger, M. Meier, M. Mirdita, H. Vöhringer, S. J. Haunsberger, and J. Söding, “Hh-suite3 for fast remote homology detection and deep protein annotation,” BMC bioinformatics, vol. 20, no. 1, pp. 1–15, 2019.
• 40. S. Bernabé, C. Garcı́a, R. Fernández-Beltran, M. E. Paoletti, J. M. Haut, J. Plaza, and A. Plaza, “Open multi-processing acceleration for unsupervised land cover categorization using probabilistic latent semantic analysis,” in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019, pp. 9835–9838.
• 41. Y. Li, E. Fadda, D. Manerba, R. Tadei, and O. Terzo, “Reinforcement learning algorithms for online single-machine scheduling,” in 2020 15th Conference on Computer Science and Information Systems (FedCSIS). IEEE, 2020, pp. 277–283.
• 42. C. Song and V. Shmatikov, “Overlearning reveals sensitive attributes,” arXiv preprint https://arxiv.org/abs/1905.11742, 2019.
• 43. X. Ying, “An overview of overfitting and its solutions,” in Journal of physics: Conference series, vol. 1168. IOP Publishing, 2019, p. 022022.

Uwagi

1. The national program "Programme d’Investissements d’Avenir (PIA)" (as part of the France 2030 programme) under the reference ANR-18-RHUS-0004. This work is part of the Federation Hospitalo-Universitaire (FHU) Saclay and Paris Seine Nord Endeavour to PerSonalize Interventions for Sepsis (SEPSIS). This work was also supported by ANR PIA funding: ANR-20-IDEES-0002 and by the iRECORDS project, funded by ERA PerMed (JTC_2021) to KZ and DA (ANR-21-PERM-0005)”.

2. Thematic Tracks Regular Papers

3. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).