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首页> 外文期刊>BMC Medical Informatics and Decision Making >Automated identification of pneumonia in chest radiograph reports in critically ill patients
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Automated identification of pneumonia in chest radiograph reports in critically ill patients

机译：重症患者胸部X光片报告中的肺炎自动识别

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Background Prior studies demonstrate the suitability of natural language processing (NLP) for identifying pneumonia in chest radiograph (CXR) reports, however, few evaluate this approach in intensive care unit (ICU) patients. Methods From a total of 194,615 ICU reports, we empirically developed a lexicon to categorize pneumonia-relevant terms and uncertainty profiles. We encoded lexicon items into unique queries within an NLP software application and designed an algorithm to assign automated interpretations (‘positive’, ‘possible’, or ‘negative’) based on each report’s query profile. We evaluated algorithm performance in a sample of 2,466 CXR reports interpreted by physician consensus and in two ICU patient subgroups including those admitted for pneumonia and for rheumatologic/endocrine diagnoses. Results Most reports were deemed ‘negative’ (51.8%) by physician consensus. Many were ‘possible’ (41.7%); only 6.5% were ‘positive’ for pneumonia. The lexicon included 105 terms and uncertainty profiles that were encoded into 31 NLP queries. Queries identified 534,322 ‘hits’ in the full sample, with 2.7 ± 2.6 ‘hits’ per report. An algorithm, comprised of twenty rules and probability steps, assigned interpretations to reports based on query profiles. In the validation set, the algorithm had 92.7% sensitivity, 91.1% specificity, 93.3% positive predictive value, and 90.3% negative predictive value for differentiating ‘negative’ from ‘positive’/’possible’ reports. In the ICU subgroups, the algorithm also demonstrated good performance, misclassifying few reports (5.8%). Conclusions Many CXR reports in ICU patients demonstrate frank uncertainty regarding a pneumonia diagnosis. This electronic tool demonstrates promise for assigning automated interpretations to CXR reports by leveraging both terms and uncertainty profiles.
机译：背景技术先前的研究表明，在胸部X光片（CXR）报告中自然语言处理（NLP）可用于识别肺炎，但是，很少有人在重症监护病房（ICU）患者中评估这种方法。方法从总共194,615个ICU报告中，我们根据经验开发了一个词典，以对与肺炎相关的术语和不确定性进行分类。我们在NLP软件应用程序中将词典项目编码为唯一查询，并设计了一种算法，可基于每个报告的查询配置文件分配自动解释（“正”，“可能”或“负”）。我们评估了由医生共识解释的2466份CXR报告样本和两个ICU患者亚组（包括因肺炎和风湿病/内分泌诊断而入院的患者）的算法性能。结果大多数报告被医生一致认为是“阴性”（51.8％）。许多是“可能的”（41.7％）;只有6.5％的人表示肺炎为“阳性”。该词典包含105个术语和不确定性概要文件，它们被编码为31个NLP查询。查询在整个样本中确定了534,322个“匹配项”，每个报告有2.7±2.6个“匹配项”。一种算法，由二十个规则和概率步骤组成，根据查询配置文件为报告分配了解释。在验证集中，该算法具有92.7％的敏感性，91.1％的特异性，93.3％的阳性预测值和90.3％的阴性预测值，可将“阴性”报告与“阳性” /“可能”报告区分开。在ICU子组中，该算法还表现出良好的性能，对少数报告（5.8％）进行了错误分类。结论许多关于ICU患者的CXR报告显示，对于肺炎的诊断存在坦率的不确定性。该电子工具展示了通过利用术语和不确定性特征为CXR报告分配自动解释的希望。

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