标题： 基于患者基本特征的体外受精(IVF)累积妊娠率预测 显示英文标题

标题： In Vitro Fertilization (IVF) Cumulative Pregnancy Rate Prediction from Basic Patient Characteristics

摘要： 全世界每年有数千万女性遭受不孕症的困扰。对于许多这样的患者来说，体外受精（IVF）是最好的选择。然而，IVF费用昂贵，耗时且对身体和情感都有很高的要求。在开始IVF之前，患者通常会问的第一个问题是，根据她的基本体检信息，她怀孕的可能性有多大。 本文提出了三种预测多次取卵周期后累积妊娠率的方法。通过对11,190名患者的实验表明，首先将患者分为不同的组，然后为每个组构建支持向量机模型可以实现最佳的整体性能。我们的模型可以在患者开始实际IVF程序之前，仅凭她的基本体检信息，快速且经济地可靠估计她的累积妊娠率。这些预测可以帮助患者做出是否使用自己的卵子或捐赠卵子、需要多少次取卵周期、是否使用冷冻胚胎等最优决策。它们还将降低患者的怀孕成本和时间，并提高她的生活质量。 显示英文摘要

摘要： Tens of millions of women suffer from infertility worldwide each year. In vitro fertilization (IVF) is the best choice for many such patients. However, IVF is expensive, time-consuming, and both physically and emotionally demanding. The first question that a patient usually asks before the IVF is how likely she will conceive, given her basic medical examination information. This paper proposes three approaches to predict the cumulative pregnancy rate after multiple oocyte pickup cycles. Experiments on 11,190 patients showed that first clustering the patients into different groups and then building a support vector machine model for each group can achieve the best overall performance. Our model could be a quick and economic approach for reliably estimating the cumulative pregnancy rate for a patient, given only her basic medical examination information, well before starting the actual IVF procedure. The predictions can help the patient make optimal decisions on whether to use her own oocyte or donor oocyte, how many oocyte pickup cycles she may need, whether to use embryo frozen, etc. They will also reduce the patient's cost and time to pregnancy, and improve her quality of life.