Predictive modeling for classification of positive valence system symptom severity from initial psychiatric evaluation records.

@article{Posada2017PredictiveMF,
  title={Predictive modeling for classification of positive valence system symptom severity from initial psychiatric evaluation records.},
  author={Jos{\'e} D. Posada and Amie J. Barda and Lingyun Shi and Diyang Xue and V. M. Ruiz and Pei-Han Kuan and Neal D. Ryan and Fuchiang Rich Tsui},
  journal={Journal of biomedical informatics},
  year={2017},
  volume={75S},
  pages={
          S94-S104
        }
}
In response to the challenges set forth by the CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing, we describe a framework to automatically classify initial psychiatric evaluation records to one of four positive valence system severities: absent, mild, moderate, or severe. We used a dataset provided by the event organizers to develop a framework comprised of natural language processing (NLP) modules and 3 predictive models (two decision tree models and one Bayesian network… CONTINUE READING
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