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值得遵循的话语:使用医疗印象来确定是否需要医院前救生干预措施
Aaron C Weidman1, Zach Sedor-Schiffhauer1, Chase Zikmund1
1Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
概括
来自医院前紧急情况的医生印象可以识别需要救命干预 (LSI) 的患者. 机器学习模型准确地预测LSI需求,改善紧急医疗护理.
科学领域:
- 紧急医疗 紧急医疗
- 数据科学数据科学数据科学
- 自然语言处理自然语言处理.
背景情况:
- 医院前的紧急情况需要快速的患者评估和治疗决定.
- 这项研究调查了初步的医疗印象是否可以预测医院前救生干预 (LSI) 的需要.
研究的目的:
- 评估自由文本医疗印象在识别需要医院前救生干预 (LSI) 的患者中的有用性.
- 根据这些印象,评估机器学习模型在预测LSI需求方面的表现.
主要方法:
- 分析了12913名医院前患者 (2012-2021) 的自由文本医疗印象.
- 使用自然语言处理和术语频率-反向文档频率用于文本转换.
- 采用渐变增强机器学习模型来预测LSI及其子类别.
主要成果:
- 机器学习模型在预测整体LSI (AUC=0.793) 和子类别方面表现良好.
- 在医疗印象中确定了与高或低LSI率相关的特定关键词.
- 即使将直接LSI标签从印象中排除在外,模型性能仍然很强.
结论:
- 对自由文本医疗印象的机器学习分析有效地识别了需要医院前LSI的患者.
- 未来的应用可能包括分析911呼叫请求和使用语音到文本技术.
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