Symptom severity prediction from neuropsychiatric clinical records: Overview of 2016 CEGS N-GRID shared tasks Track 2.
Symptom severity prediction from neuropsychiatric clinical records: Overview of 2016 CEGS N-GRID shared tasks Track 2.
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DOI：
10.1016/j.jbi.2017.04.017
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发表时间：
2017-11
影响因子：
4.5
通讯作者：
Uzuner Ö
中科院分区：
文献类型：
--
作者：
Filannino M;Stubbs A;Uzuner Ö
CEGS N-GRID 2016自然语言处理共享任务的第二个轨道侧重于从神经精神临床记录中预测症状严重程度。第一次，初步精神病评估记录已被收集，去识别，注释和与科学界分享。110名研究人员组成24个小组参加了这一跟踪，并提交了65个系统运行评估。前十名的团队每个都实现了反归一化宏观平均绝对误差得分超过0.80。表现最好的系统采用了六种不同的基于机器学习的分类器的组合，获得了0.86的分数。这项任务导致一般很容易，但两个特定类别的记录：记录很少，但至关重要的积极效价信号，和记录描述的患者主要受消极而不是积极的效价。事实证明，这些情况对大多数系统来说都是非常具有挑战性的。需要进一步研究才能认为任务已经解决。总的来说，这条赛道的结果证明了数据驱动方法对症状严重程度分类任务的有效性。
The second track of the CEGS N-GRID 2016 natural language processing shared tasks focused on predicting symptom severity from neuropsychiatric clinical records. For the first time, initial psychiatric evaluation records have been collected, de-identified, annotated and shared with the scientific community. One-hundred-ten researchers organized in twenty-four teams participated in this track and submitted sixty-five system runs for evaluation. The top ten teams each achieved an inverse normalized macro-averaged mean absolute error score over 0.80. The top performing system employed an ensemble of six different machine learning-based classifiers to achieve a score 0.86. The task resulted to be generally easy with the exception of two specific classes of records: records with very few but crucial positive valence signals, and records describing patients predominantly affected by negative rather than positive valence. Those cases proved to be very challenging for most of the systems. Further research is required to consider the task solved. Overall, the results of this track demonstrate the effectiveness of data-driven approaches to the task of symptom severity classification.
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