Trick William E, Chapman Wendy W, Wisniewski Mary F, Peterson Brian J, Solomon Steven L, Weinstein Robert A
Health Outcomes Branch, Division of Healthcare Quality Promotion, National Center for Infectious Diseases, Centers for Disease Control and Prevention, Public Health Service, U.S. Department of Health and Human Services, Atlanta, Georgia, USA.
Infect Control Hosp Epidemiol. 2003 Dec;24(12):950-4. doi: 10.1086/502165.
To evaluate whether a natural language processing system, SymText, was comparable to human interpretation of chest radiograph reports for identifying the mention of a central venous catheter (CVC), and whether use of SymText could detect patients who had a CVC.
To identify patients who had a CVC, we performed two surveys of hospitalized patients. Then, we obtained available reports from 104 patients who had a CVC during one of two cross-sectional surveys (ie, case-patients) and 104 randomly selected patients who did not have a CVC (ie, control-patients).
A 600-bed public teaching hospital.
Chest radiograph reports were available from 124 of the 208 participants. Compared with human interpretation, SymText had a sensitivity of 95.8% and a specificity of 98.7%. The use of SymText to identify case- and control-patients resulted in a sensitivity of 43% and a specificity of 98%. Successful application of SymText varied significantly by venous insertion site (eg, a sensitivity of 78% for subclavian and a sensitivity of 3.7% for femoral). Twenty-six percent of the case-patients had a femoral CVC.
Compared with human interpretation, SymText performed well in interpreting whether a report mentioned a CVC. In patient populations with less frequent CVC placement in femoral veins, the sensitivity for CVC detection likely would be higher. Applying a natural language processing system to chest radiograph reports may be a useful adjunct to other data sources to automate detection of patients who had a CVC.
评估自然语言处理系统SymText在识别胸部X光片报告中是否提及中心静脉导管（CVC）方面是否可与人工解读相媲美，以及使用SymText能否检测出有CVC的患者。
为了识别有CVC的患者，我们对住院患者进行了两项调查。然后，我们从两项横断面调查之一期间有CVC的104例患者（即病例患者）和104例随机选择的无CVC的患者（即对照患者）中获取了可用报告。
一家拥有600张床位的公立教学医院。
208名参与者中有124人有胸部X光片报告。与人工解读相比，SymText的灵敏度为95.8%，特异度为98.7%。使用SymText识别病例和对照患者的灵敏度为43%，特异度为98%。SymText的成功应用因静脉插入部位而异（例如，锁骨下静脉的灵敏度为78%，股静脉的灵敏度为3.7%）。26%的病例患者有股静脉CVC。
与人工解读相比，SymText在解读报告中是否提及CVC方面表现良好。在股静脉置管频率较低的患者群体中，CVC检测的灵敏度可能会更高。将自然语言处理系统应用于胸部X光片报告可能是其他数据源的有用辅助手段，以自动检测有CVC的患者。
