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Interweaving Domain Knowledge and Unsupervised Learning for Psychiatric Stressor Extraction from Clinical Notes

Part of the Lecture Notes in Computer Science book series (LNAI,volume 10351)

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.

Keywords

  • Domain Knowledge
  • Conditional Random Field
  • Unify Medical Language System
  • Name Entity Recognition
  • False Negative Error

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

  1. 1.

    https://www.i2b2.org/NLP/RDoCforPsychiatry.

  2. 2.

    http://www.chokkan.org/software/crfsuite/.

  3. 3.

    http://clamp.uth.edu/.

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Acknowledgement

We thank the organizers of the CEGS N-GRID 2016 challenge for providing the corpus.

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Correspondence to Hua Xu .

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Zhang, O.R., Zhang, Y., Xu, J., Roberts, K., Zhang, X.Y., Xu, H. (2017). Interweaving Domain Knowledge and Unsupervised Learning for Psychiatric Stressor Extraction from Clinical Notes. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10351. Springer, Cham. https://doi.org/10.1007/978-3-319-60045-1_41

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  • DOI: https://doi.org/10.1007/978-3-319-60045-1_41

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