A Knowledge-Based Decision Support System for In Vitro Fertilization Treatment

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 medical advice on the treatment protocol and medication adjustment for each patient visit during IVF treatment cycle. Our system is efficient in data processing and light-weighted which can be easily embedded into electronic medical record systems. Moreover, an oocyte retrieval oriented evaluation demonstrates that our system performs well in terms of accuracy of advice for the protocols and medications.