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An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
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使用多个机器学习和深度学习算法预测早期的术后PONV.

Cheng-Mao Zhou1,2, Ying Wang3, Qiong Xue3

  • 1Department of Anaesthesiology, Central People's Hospital of Zhanjiang, Zhanjiang, Guangdong, China. zhouchengmao187@foxmail.com.

BMC medical research methodology
|May 31, 2023
PubMed
概括
此摘要是机器生成的。

这项研究开发了一种人工智能模型来预测术后恶心和吐 (PONV),识别了诸如哈洛佩里多尔和患者病史等关键风险因素. 人工智能模型为早期PONV预测提供了一个有前途的工具.

关键词:
的 AUC AUC 的 AUC.深度学习是一种深度学习.机器学习是机器学习.这就是PONV的意义.在SVC中,SVC是SVC.

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科学领域:

  • 医疗信息学 医疗信息学
  • 人工智能在医学中的应用
  • 临床预测模型临床预测模型

背景情况:

  • 手术后恶心和吐 (PONV) 显著影响患者的满意度,并增加医疗保健费用由于长时间住院.
  • 早期识别和管理PONV对于改善患者的治疗结果和优化资源利用至关重要.

研究的目的:

  • 开发和评估一个初步的人工智能 (AI) 算法模型,用于PONV的早期预测.
  • 确定导致早期PONV发生的关键临床因素.

主要方法:

  • 使用R进行统计分析和Python开发机器学习预测模型.
  • 设计功能以确定早期PONV的最大贡献因素.
  • 评估了多个人工智能算法,包括CNNRNN,决策树,SVC和adaboost,以获得预测准确度,精度和AUC.

主要成果:

  • 哈洛佩里多尔的使用,患者的性别,年龄,吸烟史和之前的PONV史被确定为早期PONV的前五大预测因素.
  • CNNRNN算法显示了最高的精度 (0.872),而CNNRNN也显示了最高的精度 (1.000).
  • 后勤回归,SVC和adapboost获得了最高的AUC分数,表明了强大的预测性能.

结论:

  • 人工智能算法能够准确地预测早期的PONV.
  • 后勤回归,SVC和adapboost算法在预测PONV中表现出最好的整体性能.
  • 建立了一个公开可访问的在线工具,用于使用Streamlit应用程序预测早期PONV.