Nothing Special   »   [go: up one dir, main page]
Skip to main content
SpringerLink
Log in

Behavior patterns in hormonal treatments using fuzzy logic models

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Assisted reproductive technologies are a combination of medical strategies designed to treat infertility patients. Ideal stimulation treatment has to be individualized, but one of the main challenges which clinicians face in the everyday clinic is how to select the best medical protocol for a patient. This work aims to look for behavior patterns in this kind of treatments, using fuzzy logic models with the objective of helping gynecologists and embryologists to make decisions that could improve the process of in vitro fertilization. For this purpose, a real-world dataset composed of one hundred and twenty-three (123) patients and five hundred and fifty-nine (559) treatments applied in relation to such patients provided by an assisted reproduction clinic, has been used to obtain the fuzzy models. As conclusion, this work corroborates some known clinic experiences, provides some new ones and proposes a set of questions to be solved in future experiments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Aroba J (2003) Advances in the decision making in software development projects. Ph.D. thesis. University of Seville

  • Aroba J, Cuadrado-Gallego JJ, Sicilia M-Á, Ramos I, García-Barriocanal E (2008) Segmented software cost estimation models based on fuzzy clustering. J Syst Softw 81:1944–1950

    Article  Google Scholar 

  • Borges G, Brusamarello V (2015) Sensor fusion methods for reducing false alarms in heart rate monitoring. J Clin Monit Comput 30(6):859–867

  • Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40

    Article  Google Scholar 

  • Cotton S, Hill S, Hirstein AF, James JL (2005) Model assisted reproductive technology act. J Gend Race Justice 9:55

    Google Scholar 

  • Fukuyama Y, Sugeno M (1989) A new method of choosing the number of clusters for fuzzy means method. In: Proceedings of the 5th fuzzy systems symposium, pp 247–250

  • Furuhashi T, Suzuki T (2001) On interpretability of fuzzy models based on conciseness measure. In: The 10th IEEE international conference on fuzzy systems, 2001, pp 284–287

  • Gegúndez ME, Aroba J, Bravo JM (2008) Identification of piecewise affine systems by means of fuzzy clustering and competitive learning. Eng Appl Artif Intell 21:1321–1329

    Article  Google Scholar 

  • Glymour C, Madigan D, Pregibon D, Smyth P (1996) Statistical inference and data mining. Commun ACM 39:35–41

    Article  Google Scholar 

  • Guillaume S (2001) Designing fuzzy inference systems from data: an interpretability-oriented review. IEEE Trans Fuzzy Syst 9:426–443

    Article  Google Scholar 

  • Hand DJ (1998) Data mining: statistics and more? Am Stat 52:112–118

    Google Scholar 

  • Hathaway RJ, Bezdek JC (1993) Switching regression models and fuzzy clustering. IEEE Trans Fuzzy Syst 1:195–204

    Article  Google Scholar 

  • Hoppner F, Klawonn F (2003) A contribution to convergence theory of fuzzy c-means and derivatives. IEEE Trans Fuzzy Syst 11(5):682–694

    Article  Google Scholar 

  • Josefiok M, Sauer J (2015) Towards an expert system for the field of neurology based on fuzzy logic. In: Joint German/Austrian conference on artificial intelligence (Knstliche Intelligenz), pp 333–340

  • Jurisica I, Mylopoulos J, Glasgow J, Shapiro H, Casper RF (1998) Case-based reasoning in IVF: prediction and knowledge mining. Artif Intell Med 12:1–24. doi:10.1016/S0933-3657(97)00037-7

    Article  Google Scholar 

  • Kaufman L, Rousseeuw PJ (2009) Finding groups in data: an introduction to cluster analysis. Wiley, New York

    MATH  Google Scholar 

  • Lawson AK, Klock SC, Pavone ME, Hirshfeld-Cytron J, Smith KN, Kazer RR (2014) Prospective study of depression and anxiety in female fertility preservation and infertility patients. Fertil Steril 102:1377–1384. doi:10.1016/j.fertnstert.2014.07.765

  • Lopes MHBM, DAncona CAL, Ortega NRS, Silveira PSP, Faleiros-Martins AC, Marin HF (2015) A fuzzy logic model for differential diagnosis of lower urinary tract dysfunctions. Int J Urol Nurs 10(3):146–153

  • Miyahira SA, Araujo E (2008) Fuzzy obesity index for obesity treatment and surgical indication. In: IEEE international conference on fuzzy systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence), pp 2392–2397

  • Morales DA, Bengoetxea E, Larraaga P, García M, Franco Y, Fresnada M, Merino M (2008) Bayesian classification for the selection of in vitro human embryos using morphological and clinical data. Comput Methods Programs Biomed 90:104–116. doi:10.1016/j.cmpb.2007.11.018

    Article  Google Scholar 

  • Nakajima H (1996) Fuzzy logic and data mining. In: Proceedings of the 1996 Asian fuzzy systems, IEEE 1996, pp 133–138

  • Panavaranan P, Wongsawat Y (2013) EEG-based pain estimation via fuzzy logic and polynomial kernel support vector machine. In: Biomedical engineering international conference (BMEiCON), 2013 6th, pp 1–4

  • Rane AL (2015) Clinical decision support model for prevailing diseases to improve human life survivability. In: 2015 international conference on pervasive computing (ICPC), pp 1–5

  • SEF (2016) Sociedad Espaola de Fertilidad. Accessed Oct 2016

  • Shamim MS, Enam SA, Qidwai U (2009) Fuzzy logic in neurosurgery: predicting poor outcomes after lumbar disk surgery in 501 consecutive patients. Surg Neurol 72:565–572

    Article  Google Scholar 

  • Sugeno M, Yasukawa T (1993) A fuzzy-logic-based approach to qualitative modeling. IEEE Trans Fuzzy Syst 1:7–31. doi:10.1109/TFUZZ.1993.390281

    Article  Google Scholar 

  • Tartarisco G, Baldus G, Corda D, Raso R, Arnao A, Ferro M, Gaggioli A, Pioggia G (2012) Personal health system architecture for stress monitoring and support to clinical decisions. Comput Commun 35:1296–1305

    Article  Google Scholar 

  • Yilmaz A, Ayan K (2011) Risk analysis in breast cancer disease by using fuzzy logic and effects of stress level on cancer risk. Sci Res Essays 6:5179–5191

    Google Scholar 

  • Yilmaz A, Ayan K (2013) Cancer risk analysis by fuzzy logic approach and performance status of the model. Turk J Electr Eng Comput Sci 21:897–912

    Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  MATH  Google Scholar 

Download references

Acknowledgements

This research has been supported by the MeGUS project (TIN2013-46928-C3-3-R), Pololas project (TIN2016-76956-C3-2-R), by the SoftPLM Network (TIN2015-71938-REDT) of the Spanish the Ministry of Economy and Competitiveness and Fujitsu Laboratories of Europe (FLE).

Author information

Authors and Affiliations

  1. Department of Computer Languages and Systems, University of Seville, Av. Reina Mercedes s/n, Seville, Spain

    J. G. Enríquez, V. Cid, N. Muntaner, F. J. Domínguez-Mayo, M. J. Escalona & I. Ramos

  2. Department of Information Technologies, University of Huelva, Avda. de las Fuerzas Armadas, Campus El Carmen, Huelva, Spain

    J. Aroba

  3. INEBIR, Hospital Victoria Eugenia, Seville, Spain

    J. Navarro

Authors
  1. J. G. Enríquez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Cid
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. N. Muntaner
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J. Aroba
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. J. Navarro
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. F. J. Domínguez-Mayo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. M. J. Escalona
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. I. Ramos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. G. Enríquez.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interests.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Enríquez, J.G., Cid, V., Muntaner, N. et al. Behavior patterns in hormonal treatments using fuzzy logic models. Soft Comput 22, 79–90 (2018). https://doi.org/10.1007/s00500-017-2614-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-017-2614-7

Keywords

Search

Navigation