A Knowledge-Based Decision Support System for In Vitro Fertilization Treatment
This addresses the need for personalized and efficient treatment adjustments in IVF to improve success rates, though it appears incremental as it builds on existing decision support approaches in medical domains.
The paper tackles the problem of optimizing In Vitro Fertilization (IVF) treatment by proposing a knowledge-based decision support system that provides medical advice on treatment protocols and medication adjustments, with an evaluation showing it performs well in accuracy for oocyte retrieval.
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.