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通过使用深度学习模型,改进扫描实验室报告中的表格数据提取.

Yiming Li¹, Qiang Wei², Xinghan Chen³

¹McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.

Journal of biomedical informatics

|October 11, 2024

概括

此摘要是机器生成的。

这项研究引入了先进的深度学习光学字符识别 (OCR) 管道,以从扫描报告中提取实验室测试结果,提高临床数据的可访问性. 新方法准确地识别和解释表格数据,增强及时的医疗保健决策.

关键词:

人工智能的人工智能深度学习是一种深度学习.电子健康记录电子健康记录提取信息提取信息自然语言处理自然语言处理.这是光学字符识别系统.

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科学领域:

医疗信息学医疗信息学
人工智能的人工智能
临床实验室科学临床实验室科学

背景情况:

医疗实验室测试对于医疗保健决策至关重要.
目前的方法经常使用传真报告,延迟数据访问.
需要有效地从扫描文件中提取实验室数据.

研究的目的:

开发基于深度学习的光学字符识别 (OCR) 方法,用于从扫描报告中识别和提取实验室测试结果.
提高临床数据可用性的及时性和准确性.

主要方法:

使用了一种两阶段的方法:表检测和表识别.
深度学习模型DETR R18和YOLOv8s被评估为表检测.
在表识别方面,PaddleOCR和编码器-双解码器 (EDD) 模型进行了比较.
来自632份报告的650个表格进行了注释,用于培训和评估.

主要成果:

微调的DETR R18在桌面检测方面取得了卓越的性能 (AP50: 0.774).
微调的EDD在表识别方面表现出强的表现 (TEDS得分:0.815).
集成的OCR管道获得了TEDS得分0.699和TEDS结构得分0.764.

结论:

开发了一种使用最先进的深度学习模型的新型OCR管道,用于扫描的临床文件.
该管道有效地提取表格式实验室测试数据,增强临床数据分析.
这种方法对通过可访问数据改善临床决策具有重大意义.