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

## TL;DR

This paper introduces a novel domain adaptation approach for sentiment analysis in psychiatric electronic health records, highlighting the challenges and potential for clinical decision support.

## Contribution

It defines psychiatric clinical sentiment, conducts an annotation project, and evaluates ML models, demonstrating the importance of domain-specific adaptation in clinical NLP.

## Key findings

- Off-the-shelf sentiment tools fail in clinical settings
- Clinical sentiment is learnable with limited data
- This work advances sentiment analysis for psychiatric EHRs

## 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.