EURO 2024 Copenhagen
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4088. Employing artificial intelligence in healthcare: a systematic approach for the detection of mental disorders via social media analysis

Invited abstract in session WA-6: Advancements of OR-analytics in statistics, machine learning and data science 16, stream Advancements of OR-analytics in statistics, machine learning and data science.

Wednesday, 8:30-10:00
Room: 1013 (building: 202)

Authors (first author is the speaker)

1. Qing Yin
Alliance Manchester Business School, University of Manchester
2. Yunya Song
Department of Journalism, Hong Kong Baptist University
3. Xian Yang
Alliance Manchester Business School, University of Manchester

Abstract

The rising prevalence of mental disorders significantly strains public healthcare services. The rapid growth of social media has created a solid foundation for research into public mental health. Artificial Intelligence (AI)-assisted methods have been developed to detect mental disorders in social media posts. However, existing studies have primarily focused on a limited array of predefined mental disorders, overlooking the continuous emergence of unknown mental disorders. This study presents an innovative method by establishing a comprehensive system based on pre-trained language models (PLMs), termed as systematic PLMs (sPLMs). This system is adept at detecting known/in-domain (IND) and unknown/out-of-domain (OOD) mental disorders, and it further specifies the names of the mental disorders detected. Comprehensive experimental assessments in a public social media dataset, the Reddit dataset, demonstrate that sPLMs outperform pure PLMs (pPLMs) in detecting OOD mental disorders, achieving a 16.54% higher average F1 score. Furthermore, sPLMs exhibit a 91.58% accuracy in generating names for OOD mental disorders. This superior performance, both quantitatively and qualitatively, paving the way for advanced AI applications in healthcare domain.

Keywords

Status: accepted

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