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.

中文翻译：

---

双相情感障碍与边缘型人格障碍的区分：一种机器学习方法

背景

双相情感障碍（BP）与边缘型人格障碍（BPD）的区分是常见的诊断难题。我们采用了机器学习（ML）的方法来区分条件。

方法

比较符合DSM BP或BPD标准的参与者在检查认知和行为BPD结构，情绪调节策略以及儿童时期父母行为的措施。两项分析使用了连续数据和二分数据，与DSM相比，ML分配的诊断结果更高。

结果

82名参与者符合BP的DSM标准，而BPD符合52项。BP的ML分类准确度为84.1％-87.8％，BPD的ML分类准确度为73.1％-73.9％。在分析之间，项目的重要性有所不同，总体上最重要的项目包括身份困难，关系问题，女性性别，关系破裂后的自杀倾向和年龄。

局限性

参与者为志愿者，双极型II（BP II）参与者占优势，未检查BP和BPD合并症，并且小BPD样本导致该组的分类准确性相对较低

结论

研究结果可能有助于根据认知和行为领域，情绪调节策略和父母行为的差异来区分BP和BPD。将来使用较大的数据集进行的研究可能会进一步提高预测准确性，并有助于进行鉴别诊断。