Radia, Inc., Lynwood, WA, USA.
Department of Biostatistics, University of Washington, Seattle, WA, USA.
J Digit Imaging. 2018 Feb;31(1):84-90. doi: 10.1007/s10278-017-0013-3.
Electronic medical record (EMR) systems provide easy access to radiology reports and offer great potential to support quality improvement efforts and clinical research. Harnessing the full potential of the EMR requires scalable approaches such as natural language processing (NLP) to convert text into variables used for evaluation or analysis. Our goal was to determine the feasibility of using NLP to identify patients with Type 1 Modic endplate changes using clinical reports of magnetic resonance (MR) imaging examinations of the spine. Identifying patients with Type 1 Modic change who may be eligible for clinical trials is important as these findings may be important targets for intervention. Four annotators identified all reports that contained Type 1 Modic change, using N = 458 randomly selected lumbar spine MR reports. We then implemented a rule-based NLP algorithm in Java using regular expressions. The prevalence of Type 1 Modic change in the annotated dataset was 10%. Results were recall (sensitivity) 35/50 = 0.70 (95% confidence interval (C.I.) 0.52-0.82), specificity 404/408 = 0.99 (0.97-1.0), precision (positive predictive value) 35/39 = 0.90 (0.75-0.97), negative predictive value 404/419 = 0.96 (0.94-0.98), and F1-score 0.79 (0.43-1.0). Our evaluation shows the efficacy of rule-based NLP approach for identifying patients with Type 1 Modic change if the emphasis is on identifying only relevant cases with low concern regarding false negatives. As expected, our results show that specificity is higher than recall. This is due to the inherent difficulty of eliciting all possible keywords given the enormous variability of lumbar spine reporting, which decreases recall, while availability of good negation algorithms improves specificity.
电子病历 (EMR) 系统可方便地获取放射学报告，并为支持质量改进工作和临床研究提供巨大潜力。要充分发挥 EMR 的潜力，需要采用自然语言处理 (NLP) 等可扩展方法，将文本转换为用于评估或分析的变量。我们的目标是确定使用 NLP 从脊柱磁共振 (MR) 成像检查的临床报告中识别 1 型 Modic 终板改变患者的可行性。识别可能有资格参加临床试验的 1 型 Modic 改变患者很重要，因为这些发现可能是干预的重要目标。四名注释员使用 N=458 份随机选择的腰椎 MR 报告，使用自然语言处理技术识别所有包含 1 型 Modic 改变的报告。然后，我们使用正则表达式在 Java 中实现了基于规则的 NLP 算法。注释数据集 1 型 Modic 改变的患病率为 10%。结果为召回率 (灵敏度) 35/50=0.70(95%置信区间[CI]0.52-0.82)，特异性 404/408=0.99(0.97-1.0)，阳性预测值 35/39=0.90(0.75-0.97)，阴性预测值 404/419=0.96(0.94-0.98)，F1 评分为 0.79(0.43-1.0)。我们的评估表明，如果重点是仅识别低假阴性风险的相关病例，则基于规则的 NLP 方法可有效识别 1 型 Modic 改变患者。如预期的那样，我们的结果表明特异性高于召回率。这是因为由于腰椎报告的巨大变异性，难以引出所有可能的关键字，这降低了召回率，而良好的否定算法的可用性提高了特异性。
