A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder
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...
| Main Authors: | , , , , |
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| Format: | Journal article |
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Springer Nature
2018
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| _version_ | 1797066923204149248 |
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| author | Arribas, I Goodwin, G Geddes, J Lyons, T Saunders, K |
| author_facet | Arribas, I Goodwin, G Geddes, J Lyons, T Saunders, K |
| author_sort | Arribas, I |
| collection | OXFORD |
| 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. |
| first_indexed | 2024-03-06T21:48:58Z |
| format | Journal article |
| id | oxford-uuid:4a96afcf-8ee7-4c35-8c92-fe3eb606d4e6 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T21:48:58Z |
| publishDate | 2018 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | oxford-uuid:4a96afcf-8ee7-4c35-8c92-fe3eb606d4e62022-03-26T15:38:22ZA signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorderJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:4a96afcf-8ee7-4c35-8c92-fe3eb606d4e6Symplectic Elements at OxfordSpringer Nature2018Arribas, IGoodwin, GGeddes, JLyons, TSaunders, KMobile 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. |
| spellingShingle | Arribas, I Goodwin, G Geddes, J Lyons, T Saunders, K A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder |
| title | A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder |
| title_full | A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder |
| title_fullStr | A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder |
| title_full_unstemmed | A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder |
| title_short | A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder |
| title_sort | signature based machine learning model for distinguishing bipolar disorder and borderline personality disorder |
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