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首页> 外文期刊>Pharmacoepidemiology and drug safety >Natural Language Processing to identify pneumonia from radiology reports.
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Natural Language Processing to identify pneumonia from radiology reports.

机译：从放射学报告中鉴定肺炎的自然语言处理。

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This study aimed to develop Natural Language Processing (NLP) approaches to supplement manual outcome validation, specifically to validate pneumonia cases from chest radiograph reports.We trained one NLP system, ONYX, using radiograph reports from children and adults that were previously manually reviewed. We then assessed its validity on a test set of 5000 reports. We aimed to substantially decrease manual review, not replace it entirely, and so, we classified reports as follows: (1) consistent with pneumonia; (2) inconsistent with pneumonia; or (3) requiring manual review because of complex features. We developed processes tailored either to optimize accuracy or to minimize manual review. Using logistic regression, we jointly modeled sensitivity and specificity of ONYX in relation to patient age, comorbidity, and care setting. We estimated positive and negative predictive value (PPV and NPV) assuming pneumonia prevalence in the source data.Tailored for accuracy, ONYX identified 25% of reports as requiring manual review (34% of true pneumonias and 18% of non-pneumonias). For the remainder, ONYX's sensitivity was 92% (95% CI 90-93%), specificity 87% (86-88%), PPV 74% (72-76%), and NPV 96% (96-97%). Tailored to minimize manual review, ONYX classified 12% as needing manual review. For the remainder, ONYX had sensitivity 75% (72-77%), specificity 95% (94-96%), PPV 86% (83-88%), and NPV 91% (90-91%).For pneumonia validation, ONYX can replace almost 90% of manual review while maintaining low to moderate misclassification rates. It can be tailored for different outcomes and study needs and thus warrants exploration in other settings. Copyright ? 2013 John Wiley & Sons, Ltd.
机译：本研究旨在开发自然语言处理（NLP）以补充手动结果验证的方法，特别是为了验证胸部Xco.Nopry报告的肺炎病例。我们训练了一个NLP系统，Onyx，使用先前手动审查的儿童和成年人的射线照片报告。然后，我们对5000个报告的测试集进行了有效性。我们的目标是大幅减少手工评审，而不是完全取代，因此，我们分类如下：（1）与肺炎一致; （2）与肺炎不一致;或（3）由于复杂的特征需要手动审查。我们开发了定制的流程，以优化准确性或最大限度地减少手动审查。使用Logistic回归，我们与患者年龄，合并症和护理环境有关的敏感性和特异性。我们估计了源于肺源数据中的肺炎普遍性的正负预测值（PPV和NPV）。on inyx确定了25％的报告，要求手工评论（34％的真正肺炎和18％的非肺炎的18％）。对于剩余的，Onyx的敏感性为92％（95％CI 90-93％），特异性87％（86-88％），PPV 74％（72-76％），NPV 96％（96-97％）。定制以最大限度地减少手工评论，Onyx分类为12％，以便在手动审查中。对于剩余的，Onyx具有敏感性75％（72-77％），特异性95％（94-96％），PPV 86％（83-88％）和NPV 91％（90-91％）。对于肺炎验证，Onyx可以替代近90％的手工评论，同时保持低至中等错误分类率。可以针对不同的结果和学习需求进行调整，从而保证其他环境中的探索。版权？ 2013年John Wiley＆Sons，Ltd。

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