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A Comparison of Two Approaches to Text Processing: Facilitating Chart Reviews of Radiology Reports in Electronic Medical Records

机译:两种文本处理方法的比较:简化电子病历中放射报告的图表审查

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摘要

Chart review is central to health services research. Text processing, which analyzes free-text fields through automated methods, can facilitate this process. We compared precision and accuracy of NegEx and SQLServer 2008 Free-Text Search in identifying acute fractures in radiology reports.The term “fracture” was included in 23,595 radiology reports from the Veterans Aging Cohort Study. Four hundred reports were randomly selected and manually reviewed for acute fractures to establish a gold standard. Reports were then processed by SQLServer and NegEx. Results were compared to the gold standard to determine accuracy, precision, recall, and F-statistic.NegEx and the gold standard identified acute fractures in 13 reports. SQLServer identified 2 in a report-based analysis (precision: 1.00; accuracy: 0.97; recall: 0.15; F-statistic: 0.26), and 12 in a sentence-by-sentence analysis (precision: 1.00; recall: 0.92; accuracy: 0.92; F-statistic: 0.96).Text-processing tools utilizing basic database or programming skills are comparable, precise, and accurate in identifying reports for review.
机译:图表审查对卫生服务研究至关重要。通过自动方法分析自由文本字段的文本处理可以促进此过程。我们比较了NegEx和SQLServer 2008自由文本搜索在放射学报告中识别急性骨折的准确性和准确性。“骨折”一词包含在退伍军人老龄化队列研究的23,595份放射学报告中。随机选择四百份报告,并人工检查急性骨折以建立金标准。然后,报表由SQLServer和NegEx处理。将结果与黄金标准进行比较以确定准确性,准确性,召回率和F统计量.NegEx和黄金标准在13份报告中确定了急性骨折。 SQLServer在基于报告的分析中确定2个(精度:1.00;准确性:0.97;回想率:0.15; F统计量:0.26),在逐句分析中确定12(精度:1.00;回想度:0.92;准确性: 0.92; F统计量:0.96)。利用基本数据库或编程技能的文本处理工具在确定要审核的报告方面具有可比性,精确性和准确性。

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