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

Brittany TaylorMollie HobensackStephanie Niño de RiveraYihong ZhaoRuth Marie Masterson CreberKenrick D Cato
Published in: JMIR nursing (2024)
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.
Keyphrases
  • machine learning
  • depressive symptoms
  • sleep quality
  • single cell
  • big data
  • electronic health record
  • healthcare
  • artificial intelligence
  • mental health
  • palliative care
  • physical activity