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Journal article

A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder

Abstract:

Mobile technologies offer new opportunities for prospective, high resolution monitoring of long-term health conditions. The opportunities seem of particular promise in psychiatry where diagnoses often rely on retrospective and subjective recall of mood states. However, deriving clinically meaningful information from the complex time series data these technologies present is challenging, and the current implications for patient care are uncertain. In this study, 130 participants with bipolar d...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1038/s41398-018-0334-0

Authors


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Institution:
University of Oxford
Division:
MPLS Division
Department:
Mathematical Institute
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Psychiatry
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Psychiatry
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Mathematical Institute
Oxford college:
St Anne's College
Role:
Author
ORCID:
0000-0002-9972-2809
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Psychiatry
Role:
Author
ORCID:
0000-0003-3448-9927
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Funding agency for:
Arribas, I
More from this funder
Funding agency for:
Lyons, T
Grant:
EP/N510129/1
More from this funder
Funding agency for:
Saunders, K
Grant:
Strategic Award (CONBRIO: Collaborative Oxford Network for Bipolar Research to Improve Outcomes, 102616/Z)
More from this funder
Funding agency for:
Saunders, K
Grant:
Strategic Award (CONBRIO: Collaborative Oxford Network for Bipolar Research to Improve Outcomes, 102616/Z)
Publisher:
Springer Nature Publisher's website
Journal:
Translational Psychiatry Journal website
Volume:
8
Article number:
274
Publication date:
2018-12-13
Acceptance date:
2018-09-07
DOI:
EISSN:
2158-3188
Source identifiers:
907601
Keywords:
Pubs id:
pubs:907601
UUID:
uuid:4a96afcf-8ee7-4c35-8c92-fe3eb606d4e6
Local pid:
pubs:907601
Deposit date:
2018-09-11

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