@inproceedings{33f5a8ff92ab42f1abce58dd39561bdd,
title = "Interweaving domain knowledge and unsupervised learning for psychiatric stressor extraction from clinical notes",
abstract = "Mental health is an increasingly important problem in healthcare. Psychiatric stressors are one of the major contributors of mental disorders. Very few studies have investigated stressor data in electronic health records, mostly because they are recorded in narrative texts. This study takes the initiative to develop a natural language processing system to automatically extract psychiatric stressors from clinical notes. Our approach integrates domain knowledge from multiple sources and unsupervised word representation features generated from deep learning based algorithms, to address the context dependence and data sparseness challenges caused by idiosyncratic psychosocial backgrounds. Experimental results on psychiatric notes from the CEGS N-GRID 2016 challenge demonstrate that the proposed approach is promising. The best performing configuration achieved a precision of 90.5%, a recall of 65.5%, and a F-measure of 76.0% for inexact matching.",
author = "Zhang, {Olivia R.} and Yaoyun Zhang and Jun Xu and Kirk Roberts and Zhang, {Xiang Y.} and Hua Xu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 30th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, IEA/AIE 2017 ; Conference date: 27-06-2017 Through 30-06-2017",
year = "2017",
doi = "10.1007/978-3-319-60045-1_41",
language = "English (US)",
isbn = "9783319600444",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "396--406",
editor = "Moonis Ali and Salem Benferhat and Karim Tabia",
booktitle = "Advances in Artificial Intelligence",
}