دورية أكاديمية
أعرض في EDS

Identifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review.

التفاصيل البيبلوغرافية
العنوان: Identifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review.
المؤلفون: Taylor B; School of Nursing, Columbia University, New York, NY, United States., Hobensack M; Brookdale Department of Geriatrics and Palliative Care, Icahn School of Medicine, Mount Sinai Health System, New York, NY, United States., Niño de Rivera S; School of Nursing, Columbia University, New York, NY, United States., Zhao Y; School of Nursing, Columbia University, New York, NY, United States., Masterson Creber R; School of Nursing, Columbia University, New York, NY, United States., Cato K; School of Nursing, University of Pennsylvania, Philadelphia, PA, United States.
المصدر: JMIR nursing [JMIR Nurs] 2024 Jul 19; Vol. 7, pp. e54810. Date of Electronic Publication: 2024 Jul 19.
نوع المنشور: Journal Article; Review
اللغة: English
بيانات الدورية: Publisher: JMIR Publications Inc Country of Publication: Canada NLM ID: 101771299 Publication Model: Electronic Cited Medium: Internet ISSN: 2562-7600 (Electronic) Linking ISSN: 25627600 NLM ISO Abbreviation: JMIR Nurs Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Toronto, ON, Canada : JMIR Publications Inc., [2018]-
مواضيع طبية MeSH: Machine Learning* , Depression*/genetics , Depression*/diagnosis, Humans ; Genomics ; Epigenomics/methods
مستخلص: Background: 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.
Objective: 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.
Methods: 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.
Results: 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.
Conclusions: 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.
(©Brittany Taylor, Mollie Hobensack, Stephanie Niño de Rivera, Yihong Zhao, Ruth Masterson Creber, Kenrick Cato. Originally published in JMIR Nursing (https://nursing.jmir.org), 19.07.2024.)
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معلومات مُعتمدة: R01 NS123639 United States NS NINDS NIH HHS; TL1 TR001875 United States TR NCATS NIH HHS
فهرسة مساهمة: Keywords: depression; machine learning; mental health; nurses; omics; review
تواريخ الأحداث: Date Created: 20240719 Date Completed: 20240719 Latest Revision: 20240806
رمز التحديث: 20240806
مُعرف محوري في PubMed: PMC11297379
DOI: 10.2196/54810
PMID: 39028994
قاعدة البيانات: MEDLINE
الوصف
تدمد:2562-7600
DOI:10.2196/54810