- A scoping review published in JMIR Mental Health explored how natural language processing (NLP) methods have been used by researchers to better understand bipolar disorder and identified opportunities for further NLP use.
Researchers gathered 35 studies that described the application of an NLP method to the study of bipolar disorder. They used narrative synthesis to map the literature according to four research questions. These questions related to trends that could be observed, NLP methods that have been used, the clinical and practical applications reported, and the ethical considerations present in the literature.
In terms of trends, researchers noted that there had been an increase in the publication of articles analyzing NLP and bipolar disorder, from one study in 2004 to five in 2020. Since 2015, interest in the topic has peaked and remained relatively constant. The primary objectives of the studies could be broken down into four categories: prediction and classification, characterization of the language of bipolar disorder, use of EHRs to measure health outcomes, and use of EHRs for phenotyping. The most prominent objective was prediction and classification related to bipolar disorder, and the second most prevalent was to characterize the language of bipolar disorder.
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