A Suicide Monitoring and Crisis Intervention Strategy Based on Knowledge Graph Technology for “Tree Hole” Microblog Users in China

Yang, Bing Xiang; Lin, Xia; Liu, Lianzhong; Nie, Wentao; Liu, Qian; Li, Xin Yi; Ao, Meng Qin; Wang, Xiao Qin; Xie, Ya Dian; Liu, Zhongchun; Huang, Yi Jia; Huang, Zhisheng; Gong, Xuan; Luo, Dan

doi:10.3389/fpsyg.2021.674481

Cited by 4 publications

(9 citation statements)

References 15 publications

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“…First, the Suicide Artificial Intelligence Prediction Heuristic (SAIPH) algorithm was able to accurately predict future suicidal ideation [19]. In the second study, similar results were demonstrated after adopting "The tree hole action" algorithm [20], which eventually played a significant role in preventing suicides. Graham et al [16] report that ML and NLP prove to be useful in classification of various mental health disorders such as schizophrenia, depression, and suicidal ideation.…”

Section: Clinical Outcomesmentioning

confidence: 78%

“…A combination of NLP and ML was used in four studies [11][12][13][14]. ML as the exclusive AI technique was used in four studies [15][16][17][18], artificial neural network (ANN) was used in three studies [19][20][21], and NLP in two studies [9,22].…”

Section: Ai Toolsmentioning

confidence: 99%

“…Another study by Yang et al (2021) investigated the risk of suicide among users of the website "Zou Fan Tree Hole" which was engaged in providing suicide crisis intervention to people at high risk for suicide (level 6-10). The Tree Hole Intelligent Agent is an Artificial Intelligence (AI) Program that eliminates a large portion of superfluous information and allows easy interpretation of data contained on the website, Tree-Hole.…”

Section: Suicidal Ideation and Risk Of Suicidesmentioning

confidence: 99%

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Use of Artificial Intelligence-Based Strategies for Assessing Suicidal Behavior and Mental Illness: A Literature Review

Khan¹,

Javed²

2022

Cureus

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Mental illness leading to suicide attempts is prevalent in a large portion of the population especially in low and middle-income nations. There remains a significant social stigma associated with mental illness that can lead to stigmatization of patients. Hence, patients are reluctant to communicate their problems to health care providers. Physicians have difficulty in timely identification of patients at risk for suicide. Novel and rigorously designed strategies are needed to determine the population at risk for suicide. This would be the first step in overcoming the multitude of barriers in the management of mental illness. Clinical tools and the use of electronic medical records (EMR) are time intensive. Recently, several artificial intelligence (AI)-based predictive technologies have gained momentum. The aim of this review is to summarize the recent advances in this landscape.

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Section: Clinical Outcomesmentioning

confidence: 78%

Section: Ai Toolsmentioning

confidence: 99%

Section: Suicidal Ideation and Risk Of Suicidesmentioning

confidence: 99%

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Use of Artificial Intelligence-Based Strategies for Assessing Suicidal Behavior and Mental Illness: A Literature Review

Khan¹,

Javed²

2022

Cureus

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“…Happy Jump off buildings (78), really (40), jump down (22), alive (20), burn charcoal (18), sad (18), hope (16), world (16), like (14), feel ( general comments (13). Suicide-related words such as jump off buildings, slash wrist, burn charcoal were high-frequency keywords in both but were mentioned more frequently in high suicide risk comments than general comments (13).…”

Section: What This Study Adds To Current Literaturementioning

confidence: 99%

“…High suicide risk users are people who send high suicide risk messages. The “Tree Hole Action” started by Huang provides proactive suicide crisis intervention for high suicide risk users, and it has temporarily prevented 3,629 potential suicides from 2018 to 2020 ( 14 ).…”

Section: Introductionmentioning

confidence: 99%

Characteristics of High Suicide Risk Messages From Users of a Social Network—Sina Weibo “Tree Hole”

Yang

Chen

et al. 2022

Front. Psychiatry

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BackgroundPeople with suicidal ideation post suicide-related information on social media, and some may choose collective suicide. Sina Weibo is one of the most popular social media platforms in China, and “Zoufan” is one of the largest depression “Tree Holes.” To collect suicide warning information and prevent suicide behaviors, researchers conducted real-time network monitoring of messages in the “Zoufan” tree hole via artificial intelligence robots.ObjectiveTo explore characteristics of time, content and suicidal behaviors by analyzing high suicide risk comments in the “Zoufan” tree hole.MethodsKnowledge graph technology was used to screen high suicide risk comments in the “Zoufan” tree hole. Users' level of activity was analyzed by calculating the number of messages per hour. Words in messages were segmented by a Jieba tool. Keywords and a keywords co-occurrence matrix were extracted using a TF-IDF algorithm. Gephi software was used to conduct keywords co-occurrence network analysis.ResultsAmong 5,766 high suicide risk comments, 73.27% were level 7 (suicide method was determined but not the suicide date). Females and users from economically developed cities are more likely to express suicide ideation on social media. High suicide risk users were more active during nighttime, and they expressed strong negative emotions and willingness to end their life. Jumping off buildings, wrist slashing, burning charcoal, hanging and sleeping pills were the most frequently mentioned suicide methods. About 17.55% of comments included suicide invitations. Negative cognition and emotions are the most common suicide reason.ConclusionUsers sending high risk suicide messages on social media expressed strong suicidal ideation. Females and users from economically developed cities were more likely to leave high suicide risk comments on social media. Nighttime was the most active period for users. Characteristics of high suicide risk messages help to improve the automatic suicide monitoring system. More advanced technologies are needed to perform critical analysis to obtain accurate characteristics of the users and messages on social media. It is necessary to improve the 24-h crisis warning and intervention system for social media and create a good online social environment.

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Digital Medical Information Services Delivered by Pharmaceutical Companies via WeChat: Qualitative Analytical Study

Ge,

Yao,

Ung

et al. 2023

J Med Internet Res

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Background Social media has become one of the primary information sources for medical professionals and patients. Pharmaceutical companies are committed to using various social media platforms to provide stakeholders with digital medical information services (DMISs), which remain experimental and immature. In China, WeChat tops the list of popular social media platforms. To date, little is known about the service model of DMISs delivered by pharmaceutical companies via WeChat. Objective This study aims to explore the emerging service model of DMISs delivered by pharmaceutical companies via WeChat in China. Methods This study applied a qualitative research design combining case study and documentary analysis to explore the DMISs of 6 leading pharmaceutical companies in China. Materials were collected from their official WeChat platforms. Thematic analysis was conducted on the data. Results The DMISs of 6 pharmaceutical companies were investigated. Themes emerged regarding 2 essential information services delivered by pharmaceutical companies via WeChat: business operation services and DMISs (ie, public information services, professional services, science and education services, and e-commerce services). Business operation services mainly function to assist or facilitate the company’s operations and development trends for general visitors. Public-oriented information services are realized through health science popularization, academic frontiers, product information, and road maps to hospitals and pharmacies. Internet hospital and pharmacy services are the main patient-oriented professional services. Medical staff–oriented science and education services commonly include continuing education, clinical assistance, academic research, and journal searching. Public-oriented e-commerce services include health products and health insurance. Conclusions Pharmaceutical companies in China use WeChat to provide stakeholders with diversified DMISs, which remain in the exploratory stage. The service model of DMISs requires more distinct innovations to provide personalized digital health and patient-centric services. Moreover, specific regulations on the DMISs of pharmaceutical companies need to be established to guard public health interests.

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A Suicide Monitoring and Crisis Intervention Strategy Based on Knowledge Graph Technology for “Tree Hole” Microblog Users in China

Cited by 4 publications

References 15 publications

Use of Artificial Intelligence-Based Strategies for Assessing Suicidal Behavior and Mental Illness: A Literature Review

Use of Artificial Intelligence-Based Strategies for Assessing Suicidal Behavior and Mental Illness: A Literature Review

Characteristics of High Suicide Risk Messages From Users of a Social Network—Sina Weibo “Tree Hole”

Digital Medical Information Services Delivered by Pharmaceutical Companies via WeChat: Qualitative Analytical Study

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