Mental Health Question and Answering System Based on Bert Model and Knowledge Graph Technology

Guo, Chaohui; Lin, Shaofu; Huang, Zhisheng; Yao, Yahong

doi:10.1145/3500931.3501011

Cited by 2 publications

(1 citation statement)

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“…Nearly half of depression patients have a sense of stigma [8,9], which leads depression patients to be reluctant to seek help, delay in their illness, and even commit suicide [10]. With the continuous popularization of Internet terminals, more and more people like to confide their emotions in the virtual world [11], especially those with depression [12]. Usually, we call the social media where people with depression focus on their emotions as "Tree Hole.…”

Section: Introductionmentioning

confidence: 99%

Analysis of sentiment changes in online messages of depression patients before and during the COVID-19 epidemic based on BERT+BiLSTM

Guo

Lin

Huang

et al. 2022

Health Inf Sci Syst

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With the development of the Internet, more and more people prefer to confide their sentiments in the virtual world, especially those with depression. The social media where people with depression collectively leave messages is called the "Tree Hole". The purpose of this article is to support the "Tree Hole" rescue volunteers to help patients with depression, especially after the outbreak of COVID-19 and other major events, to guide the crisis intervention of patients with depression. Based on the message data of "Tree Hole" named "Zou Fan", this paper used a deep learning model and sentiment scoring algorithm to analyze the fluctuation characteristics sentiment of user's message in different time dimensions. Through detailed investigation of the research results, we found that the number of "Tree Hole" messages in multiple time dimensions is positively correlated to emotion. The longer the "Tree Hole" is formed, the more negative the emotion is, and the outbreak of COVID-19 and other major events have obvious effects on the emotion of the messages. In order to improve the efficiency of "Tree Hole" rescue, volunteers should focus on the long-formed "Tree Hole" and the user groups that are active in the early morning. This research is of great significance for the emotional guidance of online mental health patients, especially the crisis intervention for depression patients after the outbreak of COVID-19 and other major events.

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Section: Introductionmentioning

confidence: 99%

Analysis of sentiment changes in online messages of depression patients before and during the COVID-19 epidemic based on BERT+BiLSTM

Guo

Lin

Huang

et al. 2022

Health Inf Sci Syst

Self Cite

View full text Add to dashboard Cite

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Knowledge graphs in psychiatric research: Potential applications and future perspectives

Freidel,

Schwarz

2024

Acta Psychiatr Scand

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BackgroundKnowledge graphs (KGs) remain an underutilized tool in the field of psychiatric research. In the broader biomedical field KGs are already a significant tool mainly used as knowledge database or for novel relation detection between biomedical entities. This review aims to outline how KGs would further research in the field of psychiatry in the age of Artificial Intelligence (AI) and Large Language Models (LLMs).MethodsWe conducted a thorough literature review across a spectrum of scientific fields ranging from computer science and knowledge engineering to bioinformatics. The literature reviewed was taken from PubMed, Semantic Scholar and Google Scholar searches including terms such as “Psychiatric Knowledge Graphs”, “Biomedical Knowledge Graphs”, “Knowledge Graph Machine Learning Applications”, “Knowledge Graph Applications for Biomedical Sciences”. The resulting publications were then assessed and accumulated in this review regarding their possible relevance to future psychiatric applications.ResultsA multitude of papers and applications of KGs in associated research fields that are yet to be utilized in psychiatric research was found and outlined in this review. We create a thorough recommendation for other computational researchers regarding use‐cases of these KG applications in psychiatry.ConclusionThis review illustrates use‐cases of KG‐based research applications in biomedicine and beyond that may aid in elucidating the complex biology of psychiatric illness and open new routes for developing innovative interventions. We conclude that there is a wealth of opportunities for KG utilization in psychiatric research across a variety of application areas including biomarker discovery, patient stratification and personalized medicine approaches.

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Mental Health Question and Answering System Based on Bert Model and Knowledge Graph Technology

Cited by 2 publications

References 11 publications

Analysis of sentiment changes in online messages of depression patients before and during the COVID-19 epidemic based on BERT+BiLSTM

Analysis of sentiment changes in online messages of depression patients before and during the COVID-19 epidemic based on BERT+BiLSTM

Knowledge graphs in psychiatric research: Potential applications and future perspectives

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