Journal
JOURNAL OF BIOMEDICAL INFORMATICS
Volume 75, Issue -, Pages S105-S111Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2017.05.015
Keywords
Text classification; Gradient tree boosting; Severity prediction; Bootstrap; Psychiatric evaluation; NLP
Funding
- NIH [P50 MH106933, 4R13LM011311]
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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.
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