Comprehensive Summary
This paper, written by Scott et. al, explores the usage of clinical features and biomarkers with machine learning in determining a drug treatment plan for bipolar disorder. To analyze this, research pertaining to bipolar disorder presentation and drug effectiveness were reviewed. Clinical features such as bipolar disorder type, cycling frequency between manic and depressive stages, comorbidities, and age of onset were analyzed along with genetic, neuroimaging, and cellular biomarkers. From this study, it was concluded that the diversity of bipolar disorder clinical presentations needs to be further explored to improve the training data available for machine learning models. Additionally, this paper suggests that the net benefit of a treatment plan should be determined by two types of models - one to analyze the benefits of a treatment and one to analyze the risks of treatment - as treatment efficacy is not the only stake that patients need to consider.
Outcomes and Implications
From this article, further research needs to be done on bipolar disorder features and biomarkers, and drug-specific models should be created for both risks and benefits. Bipolar disorder affects around 1.5% of adult populations worldwide, and current methods of testing drug treatment efficacy take months due to the variable nature of bipolar disorder and the presence of spontaneous remissions among patients. Because of this, as the author suggests, more effective methods of determining treatment outcomes need to be developed, and machine-learning models, while preliminary, could potentially address this issue.