School of Nursing, Columbia University, New York, NY, United States.
Brookdale Department of Geriatrics and Palliative Care, Icahn School of Medicine, Mount Sinai Health System, New York, NY, United States.
JMIR Nurs. 2024 Jul 19;7:e54810. doi: 10.2196/54810.
Depression is one of the most common mental disorders that affects >300 million people worldwide. There is a shortage of providers trained in the provision of mental health care, and the nursing workforce is essential in filling this gap. The diagnosis of depression relies heavily on self-reported symptoms and clinical interviews, which are subject to implicit biases. The omics methods, including genomics, transcriptomics, epigenomics, and microbiomics, are novel methods for identifying the biological underpinnings of depression. Machine learning is used to analyze genomic data that includes large, heterogeneous, and multidimensional data sets.
This scoping review aims to review the existing literature on machine learning methods for omics data analysis to identify individuals with depression, with the goal of providing insight into alternative objective and driven insights into the diagnostic process for depression.
This scoping review was reported following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Searches were conducted in 3 databases to identify relevant publications. A total of 3 independent researchers performed screening, and discrepancies were resolved by consensus. Critical appraisal was performed using the Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional Studies.
The screening process identified 15 relevant papers. The omics methods included genomics, transcriptomics, epigenomics, multiomics, and microbiomics, and machine learning methods included random forest, support vector machine, k-nearest neighbor, and artificial neural network.
The findings of this scoping review indicate that the omics methods had similar performance in identifying omics variants associated with depression. All machine learning methods performed well based on their performance metrics. When variants in omics data are associated with an increased risk of depression, the important next step is for clinicians, especially nurses, to assess individuals for symptoms of depression and provide a diagnosis and any necessary treatment.
抑郁症是全球影响超过 3 亿人的最常见精神障碍之一。提供精神保健服务的专业人员短缺，护理人员是填补这一空白的关键力量。抑郁症的诊断严重依赖于自我报告的症状和临床访谈，这些方法容易受到隐性偏见的影响。组学方法，包括基因组学、转录组学、表观基因组学和微生物组学，是识别抑郁症生物学基础的新方法。机器学习用于分析包含大型、异质和多维数据集的基因组数据。
本范围综述旨在回顾组学数据分析中机器学习方法的现有文献，以识别患有抑郁症的个体，旨在为抑郁症诊断过程提供替代性的客观和驱动性见解。
本范围综述按照 PRISMA-ScR（系统评价和荟萃分析扩展的首选报告项目用于范围综述）指南进行报告。在 3 个数据库中进行了检索，以确定相关文献。共有 3 名独立研究人员进行筛选，通过共识解决分歧。使用 Joanna Briggs 研究所分析性横断面研究批判性评估清单对批判性评估进行评估。
筛选过程确定了 15 篇相关文献。组学方法包括基因组学、转录组学、表观基因组学、多组学和微生物组学，机器学习方法包括随机森林、支持向量机、k-最近邻和人工神经网络。
本范围综述的结果表明，组学方法在识别与抑郁症相关的组学变异方面表现相似。根据性能指标，所有机器学习方法的性能都很好。当组学数据中的变异与抑郁症风险增加相关时，重要的下一步是临床医生，尤其是护士，评估个体的抑郁症状，并提供诊断和任何必要的治疗。
