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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
Version:
Publisher's version

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

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Mathematical Institute
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Psychiatry
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Psychiatry
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Mathematical Institute
Oxford college:
St Annes College; St Annes College
ORCID:
0000-0002-9972-2809
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Psychiatry
ORCID:
0000-0003-3448-9927
More from this funder
Funding agency for:
Arribas, IP
More from this funder
Funding agency for:
Saunders, K
Publisher:
Springer Nature Publisher's website
Journal:
Translational Psychiatry Journal website
Volume:
8
Pages:
Article: 274
Publication date:
2018-12-13
Acceptance date:
2018-09-07
DOI:
EISSN:
2158-3188
Pubs id:
pubs:907601
URN:
uri:4a96afcf-8ee7-4c35-8c92-fe3eb606d4e6
UUID:
uuid:4a96afcf-8ee7-4c35-8c92-fe3eb606d4e6
Local pid:
pubs:907601
Keywords:

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