A hybrid model for automatic identification of risk factors for heart disease.
A hybrid model for automatic identification of risk factors for heart disease.
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DOI：
10.1016/j.jbi.2015.09.006
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发表时间：
2015-12
影响因子：
4.5
通讯作者：
Garibaldi JM
中科院分区：
文献类型：
--
作者：
Yang H;Garibaldi JM
冠状动脉疾病（CAD）是英国和世界范围内的首要死亡原因。检测相关危险因素并跟踪其随时间的进展对于CAD的早期预防和治疗具有重要意义。本文描述了一种信息提取系统，该系统是在作者参加 2014 年 i2b2/UTHealth NLP 挑战赛时开发的，用于自动识别病历中心脏病的危险因素。我们的方法依赖于多种自然语言处理 (NLP) 技术，例如机器学习、基于规则的方法和基于字典的关键词识别，以应对各种风险因素固有的复杂临床环境。我们的系统在挑战测试数据上取得了令人鼓舞的性能，总体微平均 F 测量为 0.915，这与该挑战任务的最佳系统（F 测量为 0.927）具有竞争力。
Coronary artery disease (CAD) is the leading cause of death in both the UK and worldwide. The detection of related risk factors and tracking their progress over time is of great importance for early prevention and treatment of CAD. This paper describes an information extraction system that was developed to automatically identify risk factors for heart disease in medical records while the authors participated in the 2014 i2b2/UTHealth NLP Challenge. Our approaches rely on several nature language processing (NLP) techniques such as machine learning, rule-based methods, and dictionary-based keyword spotting to cope with complicated clinical contexts inherent in a wide variety of risk factors. Our system achieved encouraging performance on the challenge test data with an overall micro-averaged F-measure of 0.915, which was competitive to the best system (F-measure of 0.927) of this challenge task.