Detection of the Prodromal Phase of Bipolar Disorder from Psychological and Phonological Aspects in Social Media
This addresses the critical problem of high misdiagnosis and suicide rates in bipolar disorder by enabling earlier detection through social media analysis.
The researchers tackled early detection of bipolar disorder by developing predictive models using psychological and phonological features from social media data, with results showing potential for improving regular assessments in primary care settings.
Seven out of ten people with bipolar disorder are initially misdiagnosed and thirty percent of individuals with bipolar disorder will commit suicide. Identifying the early phases of the disorder is one of the key components for reducing the full development of the disorder. In this study, we aim at leveraging the data from social media to design predictive models, which utilize the psychological and phonological features, to determine the onset period of bipolar disorder and provide insights on its prodrome. This study makes these discoveries possible by employing a novel data collection process, coined as Time-specific Subconscious Crowdsourcing, which helps collect a reliable dataset that supplements diagnosis information from people suffering from bipolar disorder. Our experimental results demonstrate that the proposed models could greatly contribute to the regular assessments of people with bipolar disorder, which is important in the primary care setting.