  Recently natural language processing (NLP) tools have been developed to
identify and extract salient risk indicators in electronic health records
(EHRs). Sentiment analysis, although widely used in non-medical areas for
improving decision making, has been studied minimally in the clinical setting.
In this study, we undertook, to our knowledge, the first domain adaptation of
sentiment analysis to psychiatric EHRs by defining psychiatric clinical
sentiment, performing an annotation project, and evaluating multiple
sentence-level sentiment machine learning (ML) models. Results indicate that
off-the-shelf sentiment analysis tools fail in identifying clinically positive
or negative polarity, and that the definition of clinical sentiment that we
provide is learnable with relatively small amounts of training data. This
project is an initial step towards further refining sentiment analysis methods
for clinical use. Our long-term objective is to incorporate the results of this
project as part of a machine learning model that predicts inpatient readmission
risk. We hope that this work will initiate a discussion concerning domain
adaptation of sentiment analysis to the clinical setting.
