Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2.
Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2.
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DOI：
10.1016/j.jbi.2015.07.001
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发表时间：
2015-12
影响因子：
4.5
通讯作者：
Uzuner Ö
中科院分区：
文献类型：
--
作者：
Stubbs A;Kotfila C;Xu H;Uzuner Ö
2014年i2b2/UTHealth自然语言处理共享任务的第二部分侧重于在糖尿病患者的纵向医疗记录叙述中识别与冠状动脉疾病（CAD）相关的医疗风险因素。危险因素包括高血压、高脂血症、肥胖、吸烟状况和家族史，以及糖尿病和冠心病，以及表明存在这些疾病的指标。除了识别风险因素外，2014年i2b2/UTHealth共享任务的这一轨道还研究了纵向医疗记录中风险因素的存在和进展。20个团队参加了这个赛道，并提交了49个系统运行供评估。前10名车队中有6支F1得分超过0.90,10支车队得分均超过0.87。最成功的系统使用了附加注释、外部词典、手写规则和支持向量机的组合。这条轨迹的结果表明，识别风险因素及其随时间的进展是在自动化系统的范围内。
The second track of the 2014 i2b2/UTHealth Natural Language Processing shared task focused on identifying medical risk factors related to Coronary Artery Disease (CAD) in the narratives of longitudinal medical records of diabetic patients. The risk factors included hypertension, hyperlipidemia, obesity, smoking status, and family history, as well as diabetes and CAD, and indicators that suggest the presence of those diseases. In addition to identifying the risk factors, this track of the 2014 i2b2/UTHealth shared task studied the presence and progression of the risk factors in longitudinal medical records. Twenty teams participated in this track, and submitted 49 system runs for evaluation. Six of the top 10 teams achieved F1 scores over 0.90, and all 10 scored over 0.87. The most successful system used a combination of additional annotations, external lexicons, hand-written rules and Support Vector Machines. The results of this track indicate that identification of risk factors and their progression over time is well within the reach of automated systems.