Predictive Modeling of Implantation Outcome in an In Vitro Fertilization Setting

体外受精 结果(博弈论) 人类受精 医学 计算机科学 重症监护医学 生物 怀孕 数学 数理经济学 遗传学 解剖
作者
Aslı Uyar,Ayşe Bener,H. Nadir Çıray
出处
期刊:Medical Decision Making [SAGE Publishing]
卷期号:35 (6): 714-725 被引量:81
标识
DOI:10.1177/0272989x14535984
摘要

Multiple embryo transfers in in vitro fertilization (IVF) treatment increase the number of successful pregnancies while elevating the risk of multiple gestations. IVF-associated multiple pregnancies exhibit significant financial, social, and medical implications. Clinicians need to decide the number of embryos to be transferred considering the tradeoff between successful outcomes and multiple pregnancies.To predict implantation outcome of individual embryos in an IVF cycle with the aim of providing decision support on the number of embryos transferred.Retrospective cohort study.Electronic health records of one of the largest IVF clinics in Turkey. The study data set included 2453 embryos transferred at day 2 or day 3 after intracytoplasmic sperm injection (ICSI). Each embryo was represented with 18 clinical features and a class label, +1 or -1, indicating positive and negative implantation outcomes, respectively.For each classifier tested, a model was developed using two-thirds of the data set, and prediction performance was evaluated on the remaining one-third of the samples using receiver operating characteristic (ROC) analysis. The training-testing procedure was repeated 10 times on randomly split (two-thirds to one-third) data. The relative predictive values of clinical input characteristics were assessed using information gain feature weighting and forward feature selection methods.The naïve Bayes model provided 80.4% accuracy, 63.7% sensitivity, and 17.6% false alarm rate in embryo-based implantation prediction. Multiple embryo implantations were predicted at a 63.8% sensitivity level. Predictions using the proposed model resulted in higher accuracy compared with expert judgment alone (on average, 75.7% and 60.1%, respectively).A machine learning-based decision support system would be useful in improving the success rates of IVF treatment.
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