A Knowledge-Based Decision Support System for In Vitro Fertilization Treatment
Xizhe Wang, Ning Zhang, Jia Wang, Jing Ni, Xinzi Sun, John Zhang,, Zitao Liu, Yu Cao, Benyuan Liu

TL;DR
This paper presents a lightweight, knowledge-based decision support system designed to optimize IVF treatment protocols and medication adjustments, aiming to improve success rates and reduce side effects.
Contribution
The paper introduces a novel decision support system tailored for IVF treatment that integrates seamlessly with electronic medical records and provides accurate, personalized medical advice.
Findings
System effectively recommends treatment protocols and medication adjustments.
High accuracy demonstrated in oocyte retrieval-oriented evaluation.
System is efficient and easily integrable into existing medical record systems.
Abstract
In Vitro Fertilization (IVF) is the most widely used Assisted Reproductive Technology (ART). IVF usually involves controlled ovarian stimulation, oocyte retrieval, fertilization in the laboratory with subsequent embryo transfer. The first two steps correspond with follicular phase of females and ovulation in their menstrual cycle. Therefore, we refer to it as the treatment cycle in our paper. The treatment cycle is crucial because the stimulation medications in IVF treatment are applied directly on patients. In order to optimize the stimulation effects and lower the side effects of the stimulation medications, prompt treatment adjustments are in need. In addition, the quality and quantity of the retrieved oocytes have a significant effect on the outcome of the following procedures. To improve the IVF success rate, we propose a knowledge-based decision support system that can provide…
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