METHODS: ARDSFlag applies machine learning (ML) and natural language processing (NLP) techniques to evaluate Berlin criteria by incorporating structured and unstructured data in an electronic health record (EHR) system. The study cohort includes 19,534 ICU admissions in the Medical Information Mart for Intensive Care III (MIMIC-III) database. The output is the ARDS diagnosis, onset time, and severity.
RESULTS: ARDSFlag includes separate text classifiers trained using large training sets to find evidence of bilateral infiltrates in radiology reports (accuracy of 91.9%±0.5%) and heart failure/fluid overload in radiology reports (accuracy 86.1%±0.5%) and echocardiogram notes (accuracy 98.4%±0.3%). A test set of 300 cases, which was blindly and independently labeled for ARDS by two groups of clinicians, shows that ARDSFlag generates an overall accuracy of 89.0% (specificity = 91.7%, recall = 80.3%, and precision = 75.0%) in detecting ARDS cases.
CONCLUSIONS: To our best knowledge, this is the first study to focus on developing a method to automate the detection of ARDS. Some studies have developed and used other methods to answer other research questions. Expectedly, ARDSFlag generates a significantly higher performance in all accuracy measures compared to those methods.
方法:ARDSFlag应用机器学习(ML)和自然语言处理(NLP)技术通过在电子健康记录(EHR)系统中整合结构化和非结构化数据来评估柏林标准。该研究队列包括重症监护医学信息集市III(MIMIC-III)数据库中的19,534名ICU入院。输出是ARDS诊断,发病时间,和严重性。
结果:ARDSFlag包括使用大型训练集训练的单独文本分类器,以发现放射学报告中的双侧浸润(准确度为91.9%±0.5%)和放射学报告中的心力衰竭/液体超负荷(准确度为86.1%±0.5%)和超声心动图注释(准确度为98.4%±0.3%)。一套300例的测试,两组临床医生盲目独立标记ARDS,显示ARDSFlag产生的总体准确度为89.0%(特异性=91.7%,召回率=80.3%,检测ARDS病例的准确率为75.0%)。
结论:据我们所知,这是第一项专注于开发自动化ARDS检测方法的研究。一些研究已经开发并使用其他方法来回答其他研究问题。期望,与这些方法相比,ARDSFlag在所有精度度量方面都能产生明显更高的性能。