Chia Nan University of Pharmacy & Science Institutional Repository:Item 310902800/34703
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 18074/20272 (89%)
造访人次 : 4076310      在线人数 : 600
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://ir.cnu.edu.tw/handle/310902800/34703


    標題: Artificial intelligence model to predict pregnancy and multiple pregnancy risk following in vitro fertilization-embryo transfer (IVF-ET)
    作者: Wen, Jen-Yu
    Liu, Chung-Fen
    Chung, Ming-Ting
    Tsai, Yung-Chieh
    貢獻者: Chi Mei Hospital
    Chi Mei Hospital
    Department of Sport Management, Chia Nan University of Pharmacy & Science
    Chi Mei Hospital
    日期: 2022
    上傳時間: 2023-12-11 14:06:04 (UTC+8)
    出版者: ELSEVIER TAIWAN
    摘要: Objective: To decrease multiple pregnancy risk and sustain optimal pregnancy chance by choosing suitable number of embryos during transfer, this study aims to construct artificial intelligence models to predict the pregnancy outcome and multiple pregnancy risk after IVF-ET.Materials and methods: From Jan 2010 to Dec 2019, 1507 fresh embryo transfer cycles contained 20 features were obtained. After eliminating incomplete records, 949 treatment cycles were included in the pregnancy model dataset and 380 cycles in the twin pregnancy model dataset. Six machine learning algorithms were used for model building based on the dataset which 70% of the dataset were randomly selected for training and 30% for validation. Model performances were quantified with the area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity.Results: Models built with XGBoost performed best. The pregnancy prediction model produced accuracy of 0.716, sensitivity of 0.711, specificity of 0.719, and AUC of 0.787. The multiple pregnancy prediction model produced accuracy of 0.711, sensitivity of 0.649, specificity of 0.740, and AUC of 0.732.Conclusions: The AI models provide reliable outcome prediction and could be a promising method to decrease multiple pregnancy risk after IVF-ET.(c) 2022 Taiwan Association of Obstetrics & Gynecology. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
    關聯: TAIWANESE JOURNAL OF OBSTETRICS & GYNECOLOGY, v.61, Issue 5, pp.837-846
    显示于类别:[運動管理系] 期刊論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    index.html0KbHTML246检视/开启
    j.tjog.2021.11.038.pdf3199KbAdobe PDF95检视/开启


    在CNU IR中所有的数据项都受到原著作权保护.

    TAIR相关文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈