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从口头解剖中估计死亡原因:开放反应是多余的还是协同的?
Ander Cejudo1, Arantza Casillas1, Alicia Pérez1
1HiTZ Basque Center for Language Technologies - Ixa NLP Group, University of the Basque Country (UPV/EHU), Spain(1).
Artificial intelligence in medicine
|September 6, 2023
概括
结合结构化和开放式问题的新口头尸检方法改善了死亡原因的预测. 这种方法通过在全球范围内提供更准确的死亡率数据来增强重要统计和公共卫生.
科学领域:
- 公共卫生 公共卫生
- 人口统计学 人口统计学
- 医疗信息学 医疗信息学
背景情况:
- 民事登记和重要统计系统对于公共卫生政策和法律权利至关重要.
- 死亡原因的医疗认证是理想的,但在全球三分之二的死亡中无法获得.
- 口语解剖 (VA) 是一种临时解决方案,通过采访收集数据.
研究的目的:
- 评估假设,将VA的开放响应 (OR) 叙述纳入VA中可以改善死亡原因 (CoD) 预测.
- 将现有的计算机编码口语解剖 (CCVA) 方法与使用语言模型的新方法进行比较.
- 开发和验证一种新的双模VA方法,将封闭 (CQs) 和开放响应 (OR) 数据结合起来.
主要方法:
- 将CCVA方法 (例如,Tariff 2.0) 与基于语言模型的VA开放响应叙事分析进行比较.
- 开发了一种新的双模式方法,整合了结构化的闭式问题和自由文本的开放答案.
- 使用了R中的软件实现,以实现结果的可复制性和与Tariff 2.0.0的比较.
主要成果:
- 拟议的双模式方法在死亡原因预测方面显著优于现有的关税2.0算法.
- 包含开放响应叙述数据为Cod确定提供了有价值的,以前未被充分利用的信息.
- 经验结果证明了综合方法的优越预测能力.
结论:
- 将开放响应叙述整合到口头尸检中,可以显著提高死亡原因确定的准确性.
- 新的双模VA方法提供了一个更有效的解决方案,用于在数据较差的环境中估计特定原因死亡率.
- 这项研究提供了一个可重复的软件工具,以推进全球卫生监测和重要统计数据.
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