Symptom severity classification with gradient tree boosting.
Symptom severity classification with gradient tree boosting.
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DOI：
10.1016/j.jbi.2017.05.015
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发表时间：
2017-11
影响因子：
4.5
通讯作者：
Huang Y
中科院分区：
文献类型：
--
作者：
Liu Y;Gu Y;Nguyen JC;Li H;Zhang J;Gao Y;Huang Y
在本文中，我们介绍了在 CEGS N-GRID 2016 任务 2 RDoC 分类竞赛中提交的系统。任务是根据患者初始精神病学评估中提供的文本确定患者某个领域的症状严重程度 (0-3)。我们首先将精神病学笔记预处理成半结构化问卷，并将简短答案转换为数字、二进制或分类特征。我们进一步为每个详细答案训练弱支持向量回归器（SVR），并将回归器的输出与其他特征结合起来，通过对各个音符的重新采样输入最终的梯度树增强分类器。我们最好的提交达到了 0.439 的宏观平均平均绝对误差，这意味着标准化分数为 81.75%。
In this paper, we present our system as submitted in the CEGS N-GRID 2016 task 2 RDoC classification competition. The task was to determine symptom severity (0–3) in a domain for a patient based on the text provided in his/her initial psychiatric evaluation. We first preprocessed the psychiatry notes into a semi-structured questionnaire and transformed the short answers into either numerical, binary, or categorical features. We further trained weak Support Vector Regressors (SVR) for each verbose answer and combined regressors’ output with other features to feed into the final gradient tree boosting classifier with resampling of individual notes. Our best submission achieved a macro-averaged Mean Absolute Error of 0.439, which translates to a normalized score of 81.75%.
影响因子：
4.5
作者：
Filannino M;Stubbs A;Uzuner Ö
通讯作者：
Uzuner Ö