Differentiation of bipolar disorder versus borderline personality disorder: A machine learning approach.
Bipolar disorder
Borderline personality disorder
Diagnosis
Emotion regulation
Machine learning
Journal
Journal of affective disorders
ISSN: 1573-2517
Titre abrégé: J Affect Disord
Pays: Netherlands
ID NLM: 7906073
Informations de publication
Date de publication:
01 06 2021
01 06 2021
Historique:
received:
21
01
2021
revised:
19
03
2021
accepted:
23
03
2021
pubmed:
13
4
2021
medline:
6
7
2021
entrez:
12
4
2021
Statut:
ppublish
Résumé
Differentiation of bipolar disorder (BP) from borderline personality disorder (BPD) is a common diagnostic dilemma. We undertook a machine learning (ML) approach to distinguish the conditions. Participants meeting DSM criteria for BP or BPD were compared on measures examining cognitive and behavioral BPD constructs, emotion regulation strategies, and parental behaviors during childhood. Two analyses used continuous and dichotomised data, with ML-allocated diagnoses compared to DSM. 82 participants met DSM criteria for BP and 52 for BPD. Accuracy of ML classification was 84.1% - 87.8% for BP, 50% - 57.7% for BPD, with overall accuracy of 73.1% - 73.9%. Importance of items differed between the analyses with the overall most important items including identity difficulties, relationship problems, female gender, feeling suicidal after a relationship breakdown and age. Participants were volunteers, preponderance of bipolar II (BP II) participants, comorbidity of BP and BPD not examined, and small BPD sample contributed to the relatively low classification accuracies for this group CONCLUSIONS: Study findings may assist distinguishing BP and BPD based on differences in cognitive and behavioral domains, emotion regulation strategies and parental behaviors. Future studies using larger datasets could further improve predictive accuracy and assist in differential diagnosis.
Sections du résumé
BACKGROUND
Differentiation of bipolar disorder (BP) from borderline personality disorder (BPD) is a common diagnostic dilemma. We undertook a machine learning (ML) approach to distinguish the conditions.
METHODS
Participants meeting DSM criteria for BP or BPD were compared on measures examining cognitive and behavioral BPD constructs, emotion regulation strategies, and parental behaviors during childhood. Two analyses used continuous and dichotomised data, with ML-allocated diagnoses compared to DSM.
RESULTS
82 participants met DSM criteria for BP and 52 for BPD. Accuracy of ML classification was 84.1% - 87.8% for BP, 50% - 57.7% for BPD, with overall accuracy of 73.1% - 73.9%. Importance of items differed between the analyses with the overall most important items including identity difficulties, relationship problems, female gender, feeling suicidal after a relationship breakdown and age.
LIMITATIONS
Participants were volunteers, preponderance of bipolar II (BP II) participants, comorbidity of BP and BPD not examined, and small BPD sample contributed to the relatively low classification accuracies for this group CONCLUSIONS: Study findings may assist distinguishing BP and BPD based on differences in cognitive and behavioral domains, emotion regulation strategies and parental behaviors. Future studies using larger datasets could further improve predictive accuracy and assist in differential diagnosis.
Identifiants
pubmed: 33845326
pii: S0165-0327(21)00317-7
doi: 10.1016/j.jad.2021.03.082
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
68-73Informations de copyright
Copyright © 2021. Published by Elsevier B.V.