Using Machine Learning to Predict Remission in Patients With Major Depressive Disorder Treated With Desvenlafaxine : Utiliser l'apprentissage machine pour prédire la rémission chez les patients souffrant de trouble dépressif majeur traités par desvenlafaxine.
James Russell Andrew BenoitSerdar M DursunRussell GreinerBo CaoMatthew R G BrownRaymond W LamAndrew J GreenshawPublished in: Canadian journal of psychiatry. Revue canadienne de psychiatrie (2021)
Our model, based on 26 clinical features, proved sufficient to predict DVS remission significantly better than chance. This may allow more accurate use of DVS without waiting 8 weeks to determine treatment outcome, and may serve as a first step toward changing psychiatric care by incorporating clinical assistive technologies using machine-learned models.
Keyphrases
- major depressive disorder
- bipolar disorder
- end stage renal disease
- newly diagnosed
- deep learning
- healthcare
- ejection fraction
- chronic kidney disease
- disease activity
- prognostic factors
- mental health
- genome wide
- quality improvement
- rheumatoid arthritis
- systemic lupus erythematosus
- dna methylation
- mass spectrometry
- machine learning
- affordable care act
- neural network