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A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder
(2018)
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 ...
Experiences of remote mood and activity monitoring in bipolar disorder: a qualitative study
(2017-01)
Mobile technology enables high frequency mood monitoring and automated passive collection of date(e.g.actigraphy) from patients more efficiently and less intrusively than has previously been possible. Such techniques are ...
Effect of lithium on circadian rhythm in bipolar disorder: A systematic review and meta-analysis
(2021-03)
Circadian rhythm disruption is commonly reported in patients with bipolar disorder. Lithium has been suggested to have effects on the circadian clock, the biological basis of the circadian rhythm. The objective of the ...
Psychosocial markers of age at onset in bipolar disorder: a machine learning approach
(2022-07)
Bipolar disorder is a chronic and severe mental health disorder. Early stratification of individuals into subgroups based on age at onset (AAO) has the potential to inform diagnosis and early intervention. Yet, the ...
Characterizing Affective Variability in Bipolar Disorder and Borderline Personality Disorder, and the Effects of Lithium, Using a Generative Model of Affect
(2022-02)
The affective variability of Bipolar Disorder (BD) is thought to qualitatively differ from that of Borderline Personality Disorder (BPD), with changes in affect persisting for longer in BD. However, quantitative studies ...