PlumX Metrics
Embed PlumX Metrics

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

Translational Psychiatry, ISSN: 2158-3188, Vol: 8, Issue: 1, Page: 274
2018
  • 67
    Citations
  • 0
    Usage
  • 187
    Captures
  • 1
    Mentions
  • 5
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    67
  • Captures
    187
  • Mentions
    1
    • News Mentions
      1
      • 1
  • Social Media
    5
    • Shares, Likes & Comments
      5
      • Facebook
        5

Most Recent News

Smartphone App Differentiates Bipolar Disorder, BPD

Researchers in the UK have developed a smartphone app enabling patients to briefly characterize their current mood daily, as well as a machine learning model to analyze the data as their moods evolve. Medscape Medical News

Article Description

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 disorder (n = 48) or borderline personality disorder (n = 31) and healthy volunteers (n = 51) completed daily mood ratings using a bespoke smartphone app for up to 1 year. A signature-based learning method was used to capture the evolving interrelationships between the different elements of mood and exploit this information to classify participants’ diagnosis and to predict subsequent mood. The three participant groups could be distinguished from one another on the basis of self-reported mood using the signature methodology. The methodology classified 75% of participants into the correct diagnostic group compared with 54% using standard approaches. Subsequent mood ratings were correctly predicted with >70% accuracy. Prediction of mood was most accurate in healthy volunteers (89–98%) compared to bipolar disorder (82–90%) and borderline personality disorder (70–78%). The signature method provided an effective approach to the analysis of mood data both in terms of diagnostic classification and prediction of future mood. It also highlighted the differing predictability and the overlap inherent within disorders. The three cohorts offered internally consistent but distinct patterns of mood interaction in their reporting which have the potential to enable more efficient and accurate diagnoses and thus earlier treatment.

Bibliographic Details

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know