Distinguishing Clinical Sentiment: The Importance of Domain Adaptation in Psychiatric Patient Health Records

Eben Holderness, Philip Cawkwell, Kirsten Bolton, James Pustejovsky, Mei-Hua Hall


Abstract
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.
Anthology ID:
W19-1915
Volume:
Proceedings of the 2nd Clinical Natural Language Processing Workshop
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Anna Rumshisky, Kirk Roberts, Steven Bethard, Tristan Naumann
Venue:
ClinicalNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
117–123
Language:
URL:
https://aclanthology.org/W19-1915
DOI:
10.18653/v1/W19-1915
Bibkey:
Cite (ACL):
Eben Holderness, Philip Cawkwell, Kirsten Bolton, James Pustejovsky, and Mei-Hua Hall. 2019. Distinguishing Clinical Sentiment: The Importance of Domain Adaptation in Psychiatric Patient Health Records. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, pages 117–123, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
Distinguishing Clinical Sentiment: The Importance of Domain Adaptation in Psychiatric Patient Health Records (Holderness et al., ClinicalNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-1915.pdf