[多模式数据驱动预测妇科手术持续时间]
Yong Huang1, Zhilin Yong1, Banghua Wu1
1（ 610066） West China Second University Hospital, Sichuan University, Chengdu 610066, China.
概括
我们开发了一个AI模型,使用手术文本来预测妇科手术的持续时间. 该模型显著提高了预测准确度,有助于更好地管理手术资源.
科学领域:
- 人工智能在医学中的应用
- 手术数据科学手术数据科学
- 机器学习用于医疗保健
背景情况:
- 准确预测妇科手术持续时间对于有效的手术室安排和资源分配至关重要.
- 非结构化外科手术规划文本包含有价值的信息,可以用于预测建模.
- 当前的方法可能无法充分利用外科手术笔记中的丰富数据.
研究的目的:
- 开发和验证一个预测模型,用于妇科手术的持续时间,使用从非结构化手术规划文本中提取的特征.
- 通过人工智能 (AI) 技术整合多式联网数据,包括来自文本的特征.
- 评估文本衍生特征对各种机器学习算法的准确性的影响.
主要方法:
- 收集了34614名接受妇科手术的患者的临床数据.
- 构建了一个嵌入式变压器模型,将手术规划文本转换为数值"步骤特征".
- 使用多个回归算法 (例如,XGBoost,随机森林) 评估了步骤特征的预测值,并使用MAE,RMSE和R-squared指标比较了带有和没有特征的性能.
主要成果:
- 从外科手术文本中获得的"步骤特征"证明了最高的预测贡献.
- 结合步骤功能的 eXtreme Gradient Boosting (XGBoost) 模型实现了最佳的性能.
- 预测准确度显著提高,R平方增加了40.43%,MAE和RMSE分别减少了21.27%和20.13%.
结论:
- 开发的嵌入式变压器模型有效地从手术规划文本中提取预测特征.
- 整合这些来自文本的功能可以提高机器学习模型的性能,以预测手术持续时间.
- XGBoost预测模型可以支持精细的妇科手术管理,并提高手术资源利用效率.
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