在复杂的心脏护理环境中生成风险降低分析 (GR2AC3E):先天性导管治疗中的风险预测
Brian P Quinn1, Lauren C Gunnelson1, Alex Case2
1Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts.
概括
机器学习模型可以预测先天性心脏导管 (CCC) 程序中的风险. 这些先进的分析通过识别针对性干预的高风险病例来提高患者安全.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 传统的统计方法与先天性心脏导管 (CCC) 风险评估的复杂性作斗争.
- 人工智能 (AI) 和机器学习 (ML) 为分析CCC中复杂的患者数据提供了先进的功能.
- 这项研究旨在利用监督的ML来更深入地了解CCC患者的风险.
研究的目的:
- 将监督的ML技术应用于企业级数据集.
- 增强对接受CCC的患者患者,程序和系统水平风险的理解.
- 开发CCC中不良事件的预测模型.
主要方法:
- 利用波士顿儿童医院电子健康记录 (2019-2020) 的综合数据集.
- 使用监督ML开发和比较随机森林和最小绝对收缩和选择操作员 (LASSO) 模型.
- 在75%的数据上训练模型,在25%的数据上验证,用ROC曲线和校准图表评估性能.
主要成果:
- 分析包括1424个CCC病例.
- 随机森林和LASSO模型都显示出预测能力 (AUC为0.67和0.68).
- 拉索模型在预测不良事件方面显示出卓越的校准性.
结论:
- 使用ML进行增强的程序前风险评估可以为临床决策提供信息.
- 针对高风险CCC患者可以实施有针对性的风险减轻策略.
- 对风险因素的更好理解旨在提高CCC患者的治疗结果.
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