.png){kind=icon}
返回
Symptom severity classification with gradient tree boosting
.png){kind=icon}
OA
AI
DOI：10.1016/j.jbi.2017.05.015.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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%. (C) 2017 Elsevier Inc. All rights reserved.
Keyword:
Text classification
Gradient tree boosting
Severity prediction
Bootstrap
Psychiatric evaluation
NLP
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.5
论文数:
3.5K
被引数:
1.9W
机构
引用论文
.png){kind=icon}
Synthesis of LiFePO4 nanoplatelets as cathode materials for Li-ion batteries锂离子电池正极材料LiFePO4纳米片的合成
PREAI
分享收藏