This preprint is under consideration at International Journal of Bipolar Disorders. 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.
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Research
Anne Duffy
University of Oxford
Corresponding Author
ORCiD: https://orcid.org/0000-0002-5895-075X
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Bipolar disorder onset peaks over early adulthood and confirmed family history is a robust risk factor. However, penetrance within families varies and most children of bipolar parents will not develop the illness. Individualized risk prediction would be helpful for identifying those young people most at risk and to inform targeted intervention. Using prospectively collected data from the Canadian Flourish High-Risk Offspring cohort study available in routine practice, we explored the use of a neural network, known as the Partial Logistic Artificial Neural Network (PLANN) to predict the time to diagnosis of bipolar spectrum disorders
Results
Overall, for predictive performance, PLANN outperformed the more traditional logistic model for one year, three year and five-year predictions. PLANN was better able to discriminate or rank individuals based on their risk of developing bipolar disorder, better able to predict the probability of developing bipolar disorder and had higher accuracy than the logistic model.
Conclusions
This evaluation of PLANN is a useful step in the investigation of using neural networks as tools in the prediction of diagnosis of mental health for at-risk individuals and demonstrated the potential that neural networks have in this field. Future research is needed to replicate these findings in a separate high-risk sample.
Keywords
Bipolar disorder, prediction, neural network, longitudinal
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On 20 May, 2021
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On 20 May, 2021
Editor invited
On 20 May, 2021
Editorial decision: Major Revision
On 08 Mar, 2021
First submitted
On 21 Dec, 2020
This version was not preprinted
Version (no preprint)
Posted 13 May, 2021
Editorial decision: Minor Revision
On 13 May, 2021
Review #1 received
Received 09 May, 2021
Reviewer #1 agreed
On 26 Apr, 2021
Reviewers invited
Invitations sent on 25 Apr, 2021
Editor assigned
On 19 Apr, 2021
Submission checks complete
On 19 Apr, 2021
Editor invited
On 19 Apr, 2021
First submitted
On 19 Apr, 2021
This version was not preprinted
Version 1
Posted 29 Dec, 2020
No community comments so far
Editorial decision: Major Revision
On 07 Mar, 2021
Review #2 received
Received 05 Mar, 2021
Reviewer #2 agreed
On 30 Jan, 2021
Review #1 received
Received 15 Jan, 2021
Reviewer #1 agreed
On 27 Dec, 2020
Reviewers invited
Invitations sent on 22 Dec, 2020
Editor assigned
On 21 Dec, 2020
Submission checks complete
On 21 Dec, 2020
Editor invited
On 21 Dec, 2020
First submitted
On 20 Dec, 2020
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Subject Areas
Psychiatry
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This preprint is under consideration at International Journal of Bipolar Disorders. 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
Anne Duffy
University of Oxford
Corresponding Author
ORCiD: https://orcid.org/0000-0002-5895-075X
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 Review
Version (no preprint)
Posted 21 May, 2021
This version was not preprinted
Version (no preprint)
Posted 20 May, 2021
Editor assigned
On 20 May, 2021
Submission checks complete
On 20 May, 2021
Editor invited
On 20 May, 2021
First submitted
On 20 May, 2021
This version was not preprinted
Version (no preprint)
Posted 20 May, 2021
Editor assigned
On 20 May, 2021
Submission checks complete
On 20 May, 2021
Editor invited
On 20 May, 2021
Editorial decision: Major Revision
On 08 Mar, 2021
First submitted
On 21 Dec, 2020
This version was not preprinted
Version (no preprint)
Posted 13 May, 2021
Editorial decision: Minor Revision
On 13 May, 2021
Review #1 received
Received 09 May, 2021
Reviewer #1 agreed
On 26 Apr, 2021
Reviewers invited
Invitations sent on 25 Apr, 2021
Editor assigned
On 19 Apr, 2021
Submission checks complete
On 19 Apr, 2021
Editor invited
On 19 Apr, 2021
First submitted
On 19 Apr, 2021
This version was not preprinted
Version 1
Posted 29 Dec, 2020
No community comments so far
Editorial decision: Major Revision
On 07 Mar, 2021
Review #2 received
Received 05 Mar, 2021
Reviewer #2 agreed
On 30 Jan, 2021
Review #1 received
Received 15 Jan, 2021
Reviewer #1 agreed
On 27 Dec, 2020
Reviewers invited
Invitations sent on 22 Dec, 2020
Editor assigned
On 21 Dec, 2020
Submission checks complete
On 21 Dec, 2020
Editor invited
On 21 Dec, 2020
First submitted
On 20 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Psychiatry
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Bipolar disorder onset peaks over early adulthood and confirmed family history is a robust risk factor. However, penetrance within families varies and most children of bipolar parents will not develop the illness. Individualized risk prediction would be helpful for identifying those young people most at risk and to inform targeted intervention. Using prospectively collected data from the Canadian Flourish High-Risk Offspring cohort study available in routine practice, we explored the use of a neural network, known as the Partial Logistic Artificial Neural Network (PLANN) to predict the time to diagnosis of bipolar spectrum disorders
Results
Overall, for predictive performance, PLANN outperformed the more traditional logistic model for one year, three year and five-year predictions. PLANN was better able to discriminate or rank individuals based on their risk of developing bipolar disorder, better able to predict the probability of developing bipolar disorder and had higher accuracy than the logistic model.
Conclusions
This evaluation of PLANN is a useful step in the investigation of using neural networks as tools in the prediction of diagnosis of mental health for at-risk individuals and demonstrated the potential that neural networks have in this field. Future research is needed to replicate these findings in a separate high-risk sample.
Figure 1
Figure 2
Figure 3
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