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首页> 外文期刊>Radiology >Application of recently developed computer algorithm for automatic classification of unstructured radiology reports: validation study.
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Application of recently developed computer algorithm for automatic classification of unstructured radiology reports: validation study.

机译:最近开发的计算机算法在非结构化放射学报告自动分类中的应用:验证研究。

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摘要

PURPOSE: To validate the accuracy of Lexicon Mediated Entropy Reduction (LEXIMER), a new information theory-based computer algorithm developed by the authors for independent analysis and classification of unstructured radiology reports based on the presence of clinically important findings (F(T), where (T) represents "true") and recommendations for subsequent action (R(T)). MATERIALS AND METHODS: The study was approved by the Human Research Committee of the institutional review board. Consecutive de-identified radiology reports (n = 1059) comprising results of barium studies (n = 99), computed tomography (n = 107), mammography (n = 90), magnetic resonance imaging (n = 108), nuclear medicine (n = 99), positron emission tomography (n = 106), radiography (n = 212), ultrasonography (n = 131), and vascular procedures (n = 107) were independently analyzed by two radiologists and then with LEXIMER to categorize the reports into F(T) and F(T)0 (containing or not containing clinically important findings) categories and R(T) and R(T)0 (containing or not containing recommendations for subsequent action) categories. Accuracy, sensitivity, specificity, and positive and negative predictive values of LEXIMER for placing reports into F(T) and F(T)0 and R(T) and R(T)0 categories were assessed by using appropriate statistical tests. RESULTS: There was strong interobserver concordance between the two radiologists for placing radiology reports into F(T) and R(T) categories (kappa = 0.9, P < .01). For the LEXIMER program, accuracy, sensitivity, specificity, and positive and negative predictive values, respectively, were 97.5% (95% confidence interval [CI]: 96.6%, 98.5%), 98.9% (95% CI: 97.9%, 99.6%), 94.9% (95% CI: 93.1%, 96.0%), 97.5% (95% CI: 96.6%, 98.0%), and 97.7% (95% CI: 95.8%, 98.8%) for placing radiology reports into F(T) and F(T)0 categories and 99.6% (95% CI: 99.2%, 99.9%), 98.2% (95% CI: 95.0%, 99.6%), 99.9% (95% CI: 99.4%, 99.99%), 99.4% (95% CI: 96.3%, 99.9%), and 99.7% (95% CI: 98.9%, 99.9%) for placing reports into R(T) and R(T)0 categories. CONCLUSION: LEXIMER is an accurate automated engine for evaluating the percentage positivity of clinically important findings and rates of recommendation for subsequent action in unstructured radiology reports.
机译:目的:为了验证词汇中介熵减少(LEXIMER)的准确性,这是一种由作者开发的基于信息论的新计算机算法,用于基于临床重要发现的存在对非结构化放射学报告进行独立分析和分类(F(T), (T)代表“真”)和后续措施的建议(R(T))。材料与方法:该研究得到机构审查委员会人类研究委员会的批准。连续的去身份化放射学报告(n = 1059),包括钡剂研究(n = 99),计算机断层扫描(n = 107),乳房X线照片(n = 90),磁共振成像(n = 108),核医学(n = 99),正电子发射断层扫描(n = 106),放射线照相(n = 212),超声检查(n = 131)和血管操作(n = 107)由两名放射科医生分别进行独立分析,然后使用LEXIMER将报告分类为F(T)和F(T)0(包含或不包含临床重要发现)类别以及R(T)和R(T)0(包含或不包含后续操作建议)类别。使用适当的统计检验评估了LEXIMER用于将报告分为F(T)和F(T)0以及R(T)和R(T)0类别的准确性,敏感性,特异性以及正负预测值。结果:两位放射线医师之间存在很强的观察者之间的一致性,以便将放射线报告分为F(T)和R(T)类别(kappa = 0.9,P <.01)。对于LEXIMER程序,准确性,敏感性,特异性以及阳性和阴性预测值分别为97.5%(95%置信区间[CI]:96.6%,98.5%),98.9%(95%CI:97.9%,99.6) %),94.9%(95%CI:93.1%,96.0%),97.5%(95%CI:96.6%,98.0%)和97.7%(95%CI:95.8%,98.8%) F(T)和F(T)0类别以及99.6%(95%CI:99.2%,99.9%),98.2%(95%CI:95.0%,99.6%),99.9%(95%CI:99.4%, 99.99%),99.4%(95%CI:96.3%,99.9%)和99.7%(95%CI:98.9%,99.9%)将报告分为R(T)和R(T)0类别。结论:Leximer是一种准确的自动化引擎,可用于评估临床重要发现的阳性率和非结构化放射学报告中后续措施的推荐率。

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