大型基于多式模式的病理学报告的标准化与信心及其预后意义
Ethar Alzaid1, Gabriele Pergola1, Harriet Evans2,3
1Department of Computer Science, University of Warwick, Coventry, UK.
The journal of pathology. Clinical research
|November 15, 2024
概括
本研究介绍了一种大型多式联运模型,用于自动从非结构化病理报告中提取关键信息. 该模型实现了高准确性,并提供了信心分数,改善了癌症研究数据标准化.
科学领域:
- 计算病理学计算病理学
- 医疗信息学医学信息学
- 医疗保健中的人工智能
背景情况:
- 病理学报告通常存在于非结构化的自由文本形式,阻碍了数据的一致解释和利用.
- 从病理学报告中提取信息的标准化对于可靠的临床和研究应用至关重要.
研究的目的:
- 开发和评估一种使用大型多式模式从非结构化病理报告中自动提取信息的实用方法.
- 根据国家指导方针生成结构化的病理学报告,并评估提取的数据的预后价值.
主要方法:
- 利用一个大型的多式联络模型,具有上下文感知提示,从非结构化的病理报告中提取特定字段 (例如等级,大小).
- 实施了信心评分机制,以表明每个提取的字段的准确性.
- 通过精度和kappa评分评估提取性能,并通过ROC分析评估信心评分质量.
主要成果:
- 该模型实现了高提取精度 (高达0.99) 和卡帕得分 (高达0.98).
- 信任度得分有效地预测了提取正确性,ROC曲线下的面积高达0.93,可自动标记错误.
- 提取的信息显示出显著的预后相关性,证实了其临床实用性.
结论:
- 拟议的框架提供了一个强大的解决方案,用于从非结构化的病理报告中自动,准确地提取信息.
- 信任评分系统通过识别潜在的提取错误来提高可靠性.
- 从病理学报告中提取的标准化,高质量的数据对癌症研究和患者护理具有相当大的预后价值.
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