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Theoretical Study of Exponential Best-Fit: Modeling hCG for Gestational Trophoblastic Disease

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Knowledge Science, Engineering and Management (KSEM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12817))

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Abstract

With the removal of the hydatidiform mole it has been shown that the human chorionic gonadotropin (hCG) hormone levels drop exponentially in women diagnosed with Gestational Trophoblastic Disease (GTD). This papers aims to introduce a new method at forecasting the decrease of the hCG levels as this could reduce the number of weekly blood test that a patient would require throughout the one year of monitoring. The hCG levels are modeled as a vertically shifted exponential curve, and this paper proposes and demonstrates a mathematical solution to finding the best parameters for this model. The method is validated using synthetic data as well as real data, and the results show that it is reliable, with decent accuracy and speed.

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Author information

Authors and Affiliations

  1. Transilvania University, Brasov, Romania

    Arpad Kerestely & Sabin Tabirca

  2. McAfee, Cork, Ireland

    Catherine Costigan

  3. University College Cork, Cork, Ireland

    Finbarr Holland & Sabin Tabirca

Authors
  1. Arpad Kerestely
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  2. Catherine Costigan
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  3. Finbarr Holland
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  4. Sabin Tabirca
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Corresponding author

Correspondence to Arpad Kerestely .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Han Qiu

  2. Ibaraki University, Hitachi, Japan

    Cheng Zhang

  3. University of Kentucky, Lexington, KY, USA

    Zongming Fei

  4. Texas A&M University – Commerce, Commerce, TX, USA

    Meikang Qiu

  5. Princeton University, Princeton, NJ, USA

    Sun-Yuan Kung

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Kerestely, A., Costigan, C., Holland, F., Tabirca, S. (2021). Theoretical Study of Exponential Best-Fit: Modeling hCG for Gestational Trophoblastic Disease. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12817. Springer, Cham. https://doi.org/10.1007/978-3-030-82153-1_35

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  • DOI: https://doi.org/10.1007/978-3-030-82153-1_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-82152-4

  • Online ISBN: 978-3-030-82153-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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