@inproceedings{d06dec449b594dd4910e1917db163ebc,
title = "Theoretical Study of Exponential Best-Fit: Modeling hCG for Gestational Trophoblastic Disease",
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.",
keywords = "Data mining, Exponential curve fit, Least squares, Mathematical modeling",
author = "Arpad Kerestely and Catherine Costigan and Finbarr Holland and Sabin Tabirca",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 14th International Conference on Knowledge Science, Engineering and Management, KSEM 2021 ; Conference date: 14-08-2021 Through 16-08-2021",
year = "2021",
doi = "10.1007/978-3-030-82153-1\_35",
language = "English",
isbn = "9783030821524",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "426--438",
editor = "Han Qiu and Cheng Zhang and Zongming Fei and Meikang Qiu and Sun-Yuan Kung",
booktitle = "Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings",
address = "Germany",
}