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Analysis for Women’s’ Menstrual Health Disorders Using Artificial Intelligence

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Information Technology for Management: Approaches to Improving Business and Society (FedCSIS-AIST 2022, ISM 2022)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 471))

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Abstract

This paper presents some developments related to a project aiming to develop an AI-based model which can determine the possible ovulation dates as well as possibility of some health risks based on the input of a woman for a finite number of menstrual cycles. In some earlier papers, the AI schemes for some health risks, such as PMS, LPD, are already discussed. In this paper, additionally the schemes for hypothyroidism and polycystic ovary syndrome (PCOS) are presented. The model is based on a ontology of medical concepts, mathematical formulations of which are designed based on the data obtained from different users over a finite number of menstrual cycles and usual relationships among different parameters determining such concepts. The mathematical formulations of the concerned medical concepts are developed by using some notions of fuzzy linguistic labels and comparators.

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Notes

  1. 1.

    www.ovufriend.pl.

  2. 2.

    https://www.womenshealth.gov/menstrual-cycle/premenstrual-syndrome.

  3. 3.

    https://www.webmd.com/infertility-and-reproduction/guide/luteal-phase-defect.

  4. 4.

    https://progyny.com/education/female-infertility/understanding-uterine-fibroids-polyps/.

  5. 5.

    In this condition the ovaries produce an abnormal amount of androgens, that are usually present in women in small amounts [9].

  6. 6.

    Hypothyroidism means the thyroid gland does not produce enough thyroid hormones, which can lead to changes in the menstrual cycle. (https://helloclue.com/articles/cycle-a-z/hypothyroidism-and-the-menstrual-cycle).

  7. 7.

    https://qz.com/2129025/where-did-ibm-go-wrong-with-watson-health.

References

  1. Bablok, L., Dziadecki, W., Szymusik, I., et al.: Patterns of infertility in Poland - multicenter study. Neuro Endocrinol Lett. 32(6), 799–804 (2011)

    Google Scholar 

  2. Dutta, S., Wasilewski, P.: Dialogue in Hierarchical Concept Learning using Prototypes and Counterexamples. Fundamenta Informaticae 162, 17–36 (2018). https://doi.org/10.3233/FI-2018-1711

  3. Dutta, S., Skowron, A.: Concepts Approximation Through Dialogue with User. In: Mihálydeák, T., et al. (eds.) IJCRS 2019. LNCS (LNAI), vol. 11499, pp. 295–311. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22815-6_23

  4. Dutta, S., Skowron, A.: Toward a computing model dealing with complex phenomena: interactive granular computing. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds.) ICCCI 2021. LNCS (LNAI), vol. 12876, pp. 199–214. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-88081-1_15

    Chapter  Google Scholar 

  5. Fedorowicz, J., et al.: Multivariate Ovulation Window Detection at OvuFriend. In: Mihálydeák, T., et al. (eds.) IJCRS 2019. LNCS (LNAI), vol. 11499, pp. 395–408. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22815-6_31

  6. Fernandez-Llatas, C., Munoz-Gama, J., Martin, N., Johnson, O., Sepulveda, M., Helm, E.: Process mining in healthcare. In: Fernandez-Llatas, C. (ed.) Interactive Process Mining in Healthcare. HI, pp. 41–52. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-53993-1_4

    Chapter  Google Scholar 

  7. Ginsburg, K.A.: Luteal phase defect: etiology, diagnosis, and management. Endocrinol. Metabolism Clin. North Am. 21(1), 85–104 (1992). Reproductive Endocrinology. https://doi.org/10.1016/S0889-8529(18)30233-0

  8. Good, P.: Resampling methods: a practical guide to data analysis. Birkhäuser Boston (2005)

    Google Scholar 

  9. Goodman, N.F., Cobin, R.H., Futterweit, W., Glueck, J.S., Legro, R.S., Carmina, E.: American Association of Clinical Endocrinologists, American College of Endocrinology, and Androgen Excess and PCOS Society Disease State Clinical Review: Guide to the Best Practices in the Evaluation and Treatment of Polycystic Ovary Syndrome - Part 1. Endocr. Pract. 21(11), 1291–1300 (2015). https://doi.org/10.4158/EP15748.DSC

  10. Hamburg, M.A., Collins, F.S.: The Path to Personalized Medicine. New England J. Med. 363(4), 301–304 (2010). https://doi.org/10.1056/NEJMp1006304

  11. Haynes, B., Haines, A.: Barriers and bridges to evidence based clinical practice. BMJ 317(7153), 273–276 (1998). https://doi.org/10.1136/bmj.317.7153.273

    Article  Google Scholar 

  12. Jankowski, A., Skowron, A., Swiniarski, R.W.: Interactive complex granules. Fundam. Informaticae 133(2–3), 181–196 (2014). https://doi.org/10.3233/FI-2014-1070

    Article  Google Scholar 

  13. Kacprzyk, J., Owsinski, J.W., Szmidt, E., Zadrozny, S.: Fuzzy linguistic summaries for human centric analyses of sustainable development goals (sdg) related to technological innovations. In: Verdegay, J.L., Brito, J., Cruz, C. (eds.) Computational Intelligence Methodologies Applied to Sustainable Development Goals, Studies in Computational Intelligence, vol. 1036, pp. 19–35. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-97344-5_2

  14. Kacprzyk, J., Yager, R.R., Merigó, J.M.: Towards human-centric aggregation via ordered weighted aggregation operators and linguistic data summaries: a new perspective on zadeh’s inspirations. IEEE Comput. Intell. Mag. 14(1), 16–30 (2019). https://doi.org/10.1109/MCI.2018.2881641

  15. Kacprzyk, J., Zadrozny, S.: Fuzzy logic-based linguistic summaries of time series: a powerful tool for discovering knowledge on time varying processes and systems under imprecision. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 6(1), 37–46 (2016). https://doi.org/10.1002/widm.1175

  16. Kalhor, M., Yuseflo, S., Kaveii, B., Mohammadi, F., Javadi, H.: Effect of yarrow (Achillea Millefolium L.) extract on premenstrual syndrome in female students living in dormitory of Qazvin university of medical sciences. J. Medicinal Plants 18(72), 52–63 (2019). https://doi.org/10.29252/jmp.4.72.S12.52

  17. Smoley, B., Robinson, C.: Natural family planning. Am. Fam. Physician 86(10), 924–928 (2012)

    Google Scholar 

  18. Sosnowski, L., Penza, T.: Generating fuzzy linguistic summaries for menstrual cycles. Annal. Comput. Sci. Inf. Syst. 21, 119–128 (2020). https://doi.org/10.15439/2020F202

    Article  Google Scholar 

  19. Sosnowski, Ł, Szymusik, I., Penza, T.: Network of fuzzy comparators for ovulation window prediction. In: Lesot, M.-J., et al. (eds.) IPMU 2020. CCIS, vol. 1239, pp. 800–813. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50153-2_59

    Chapter  Google Scholar 

  20. Sosnowski, L., Wróblewski, J.: Toward automatic assessment of a risk of women’s health disorders based on ontology decision models and menstrual cycle analysis. In: Chen, Y., et al. (eds.) 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, 15–18 December 2021, pp. 5544–5552. IEEE (2021). https://doi.org/10.1109/BigData52589.2021.9671481

  21. Sosnowski, L., Zulawinska, J., Dutta, S., Szymusik, I., Zygula, A., Bambul-Mazurek, E.: Artificial intelligence in personalized healthcare analysis for womens’ menstrual health disorders. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M., Slezak, D. (eds.) Proceedings of the 17th Conference on Computer Science and Intelligence Systems, FedCSIS 2022, Sofia, Bulgaria, 4–7 September 2022. Annal. Comput. Sci. Inf. Syst. 30, 751–760 (2022). https://doi.org/10.15439/2022F59

  22. Wu, X., Xiao, L., Sun, Y., Zhang, J., Ma, T., He, L.: A survey of human-in-the-loop for machine learning. Future Gener. Comput. Syst. 135, 364–381 (2022). https://doi.org/10.1016/j.future.2022.05.014

    Article  Google Scholar 

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Acknowledgement

The research presented in this paper is co-financed by the EU Smart Growth Operational Program 2014-2020 under the project “Developing innovative solutions in the domain of detection of frequent intimate and hormonal health disorders in women of procreative age based on artificial intelligence and machine learning - OvuFriend 2.0”, POIR.01.01.01-00-0826/20.

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Authors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447, Warsaw, Poland

    Łukasz Sosnowski

  2. University of Warmia and Mazury in Olsztyn, Słoneczna 54, 10-710, Olsztyn, Poland

    Soma Dutta

  3. Department of Obstetrics and Gynecology, Medical University of Warsaw, Żwirki i Wigury 61, 02-091, Warsaw, Poland

    Iwona Szymusik

Authors
  1. Łukasz Sosnowski
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Correspondence to Łukasz Sosnowski .

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Editors and Affiliations

  1. University of Economics in Katowice, Katowice, Poland

    Ewa Ziemba

  2. University of Warsaw, Warsaw, Poland

    Witold Chmielarz

  3. University of Szczecin, Szczecin, Poland

    Jarosław Wątróbski

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Sosnowski, Ł., Dutta, S., Szymusik, I. (2023). Analysis for Women’s’ Menstrual Health Disorders Using Artificial Intelligence. In: Ziemba, E., Chmielarz, W., Wątróbski, J. (eds) Information Technology for Management: Approaches to Improving Business and Society. FedCSIS-AIST ISM 2022 2022. Lecture Notes in Business Information Processing, vol 471. Springer, Cham. https://doi.org/10.1007/978-3-031-29570-6_4

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