利用自然语言处理筛选活动性心力衰竭患者：全院性监测之探讨
Using Natural Language Processing to Screen Patients with Active Heart Failure: An Exploration for Hospital-wide Surveillance
摘要：
在本文中，我们提出了两种不同的方法，基于规则的方法和基于机器学习的方法，通过分析电子健康记录（EHR）自动识别活动性心力衰竭病例。对于基于规则的方法，我们从临床笔记中提取心血管数据元素，并使用心力衰竭专家提供的规则根据患者的心力衰竭状况将患者匹配到不同的颜色。准确率为69.4%，F1评分为0.729。对于机器学习方法，以临床笔记的二元组作为特征，我们尝试了四种不同的模型，而具有线性核的SVM以87.5%的准确率和0.86 F1-Score实现了最佳性能。此外，从四种不同模型之间的分类比较，我们认为，线性模型更适合这个问题。一旦我们将机器学习和基于规则的算法结合起来，我们将通过提高输出的准确性和可解释性来实现医院范围内对活动性心力衰竭的监测。
英文摘要：
In this paper, we proposed two different approaches, a rule-based approach and a machine-learning based approach, to identify active heart failure cases automatically by analyzing electronic health records (EHR). For the rule-based approach, we extracted cardiovascular data elements from clinical notes and matched patients to different colors according their heart failure condition by using rules provided by experts in heart failure. It achieved 69.4% accuracy and 0.729 F1-Score. For the machine learning approach, with bigram of clinical notes as features, we tried four different models while SVM with linear kernel achieved the best performance with 87.5% accuracy and 0.86 F1-Score. Also, from the classification comparison between the four different models, we believe that linear models fit better for this problem. Once we combine the machine-learning and rule-based algorithms, we will enable hospital-wide surveillance of active heart failure through increased accuracy and interpretability of the outputs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
