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Research Article
Joon Hwan Jang
Department of Human Systems Medicine, Seoul National University College of Medicine
Corresponding Author
Bumseok Jeong
Graduate School of Medical Science and Engineering, Korea Advanced Institute for Science and Technology
Corresponding Author
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Precise remote evaluation of both suicide risk and psychiatric disorders is critical for suicide prevention as well as psychiatric well-being during COVID-19 crisis. Using questionnaires is an alternative to labour-intensive diagnostic interviews in a large general population, but previous models for predicting suicide attempts suffer from low sensitivity. We developed and validated a graph neural network model, MindWatchNet, which increased the prediction sensitivity of suicide risk in young adults (n = 17,482 for training; n = 14,238 for testing) using multi-dimensional questionnaires and suicidal ideation within 2 weeks as the prediction target. MindWatchNet achieved the highest sensitivity of 80.9% and an area under curve of 0.877 (95% confidence interval, 0.854–0.897). We demonstrated that multi-dimensional deep features covering depression, anxiety, resilience, self-esteem, and clinico-demographic information contribute to SI prediction. MindWatchNet might be useful in the remote evaluation of suicide risk in the general population of young adults for specific situations such as the COVID-19 pandemic.
Keywords
COVID-19, deep learning, depression, suicide, graph neural network
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No competing interests reported.
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Reviewer #5 agreed
On 22 Mar, 2021
Reviewer #2 agreed
On 22 Mar, 2021
Reviewer #3 agreed
On 08 Mar, 2021
Reviewer #12 agreed
On 08 Mar, 2021
Reviewer #9 agreed
On 08 Mar, 2021
Reviewer #4 agreed
On 08 Mar, 2021
Reviewer #11 agreed
On 08 Mar, 2021
Reviewer #13 agreed
On 08 Mar, 2021
Reviewer #7 agreed
On 08 Mar, 2021
Reviewer #1 agreed
On 08 Mar, 2021
Reviewer #10 agreed
On 08 Mar, 2021
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On 03 Mar, 2021
Editor invited
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This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Joon Hwan Jang
Department of Human Systems Medicine, Seoul National University College of Medicine
Corresponding Author
Bumseok Jeong
Graduate School of Medical Science and Engineering, Korea Advanced Institute for Science and Technology
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Revision
Version 1
Posted 10 Mar, 2021
No community comments so far
Editorial decision: Major revision
On 01 Apr, 2021
Review #3 received
Received 22 Mar, 2021
Review #2 received
Received 22 Mar, 2021
Review #4 received
Received 22 Mar, 2021
Review #1 received
Received 22 Mar, 2021
Reviewer #5 agreed
On 22 Mar, 2021
Reviewer #2 agreed
On 22 Mar, 2021
Reviewer #3 agreed
On 08 Mar, 2021
Reviewer #12 agreed
On 08 Mar, 2021
Reviewer #9 agreed
On 08 Mar, 2021
Reviewer #4 agreed
On 08 Mar, 2021
Reviewer #11 agreed
On 08 Mar, 2021
Reviewer #13 agreed
On 08 Mar, 2021
Reviewer #7 agreed
On 08 Mar, 2021
Reviewer #1 agreed
On 08 Mar, 2021
Reviewer #10 agreed
On 08 Mar, 2021
Reviewers invited
Invitations sent on 04 Mar, 2021
Editor assigned
On 03 Mar, 2021
Editor invited
On 03 Mar, 2021
Submission checks complete
On 03 Mar, 2021
First submitted
On 22 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Precise remote evaluation of both suicide risk and psychiatric disorders is critical for suicide prevention as well as psychiatric well-being during COVID-19 crisis. Using questionnaires is an alternative to labour-intensive diagnostic interviews in a large general population, but previous models for predicting suicide attempts suffer from low sensitivity. We developed and validated a graph neural network model, MindWatchNet, which increased the prediction sensitivity of suicide risk in young adults (n = 17,482 for training; n = 14,238 for testing) using multi-dimensional questionnaires and suicidal ideation within 2 weeks as the prediction target. MindWatchNet achieved the highest sensitivity of 80.9% and an area under curve of 0.877 (95% confidence interval, 0.854–0.897). We demonstrated that multi-dimensional deep features covering depression, anxiety, resilience, self-esteem, and clinico-demographic information contribute to SI prediction. MindWatchNet might be useful in the remote evaluation of suicide risk in the general population of young adults for specific situations such as the COVID-19 pandemic.
Figure 1
Figure 2
Figure 3
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