Fiszman M, Haug P J
Department of Medical Informatics, LDS Hospital, University of Utah, USA.
Proc AMIA Symp. 2000:235-9.
To evaluate if a medical language processing (MLP) system is able to support real-time computerization of community-acquired pneumonia (CAP) guidelines.
Prospective validation study in the emergency department of a tertiary care facility. All the chest x-ray reports available in real-time for an admission decision during a five-week period were included. The MLP system was compared to a physician for the automatic selection of eligible patients and on the extraction of radiographic findings required by five different CAP guidelines. The gold standard comprised of three independent physicians and reliability measures were calculated. The outcome measures were the area under the receiver operated characteristic curve (AUC) for selecting eligible patients, sensitivity, positive predictive value (PPV), and specificity for the extraction of radiographic findings.
During the five-week period, 243 reports were available in real-time. The AUCs on selecting eligible CAP patients were 89.7% (CI: 84.2%, 93.7%) for the MLP system, and 93.3% (CI: 83.9%, 97.8%) for the physician. The average sensitivity, PPV, and specificity for radiographic findings that assessed localization and extension of CAP were respectively: 94%, 87%, 96% (physician); and 34%, 90%, 95% (MLP system). Both, the MLP system and the physician had average sensitivity, PPV, and specificity of 97%, 97%, and 99%, respectively, when localization was not an issue. Reliability measures for the gold standard were above 70%.
The MLP system was able to support real-time computerization of guidelines by selecting eligible patients and extracting radiographic findings that do not assess localization and extension of CAP.
评估医学语言处理（MLP）系统是否能够支持社区获得性肺炎（CAP）指南的实时计算机化。
在一家三级医疗机构的急诊科进行前瞻性验证研究。纳入了在为期五周的时间内可实时获取的所有用于入院决策的胸部X光报告。将MLP系统与一名医生进行比较，以自动选择符合条件的患者，并提取五种不同CAP指南所需的影像学检查结果。金标准由三名独立医生组成，并计算可靠性指标。结果指标包括用于选择符合条件患者的受试者操作特征曲线下面积（AUC）、敏感性、阳性预测值（PPV）以及用于提取影像学检查结果的特异性。
在为期五周的时间内，共实时获取了243份报告。MLP系统选择符合条件的CAP患者的AUC为89.7%（CI：84.2%，93.7%），医生为93.3%（CI：83.9%，97.8%）。评估CAP定位和范围的影像学检查结果的平均敏感性、PPV和特异性分别为：医生，94%、87%、96%；MLP系统，34%、90%、95%。当不涉及定位问题时，MLP系统和医生的平均敏感性、PPV和特异性分别为97%、97%和99%。金标准的可靠性指标高于70%。
MLP系统能够通过选择符合条件的患者并提取不评估CAP定位和范围的影像学检查结果来支持指南的实时计算机化。
