Machine Learning: Predicting Early Outcomes of Antidepressants in Children
Aug 30, 2022, 12:05 PM
In this podcast, we are joined by Dr. Paul Croarkin and Dr. Arjun Athreya to discuss their co-authored JCPP paper ‘Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants’.
DOI: 10.13056/acamh.20847
In this podcast, we are joined by Dr. Paul Croarkin of the Department of Psychiatry and Psychology at the Mayo Clinic Rochester, Minnesota, and Dr. Arjun Athreya of the Department of Molecular Pharmacology and Experimental Therapeutics at the same institution.
The focus of this podcast is on the JCPP paper ‘Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants’ (doi.org/10.1111/jcpp.13580).
Paul and Arjun set the scene by detailing what they looked at in this study, providing us with a summary of the paper, plus sharing insights into the methodology used for the research, before turning to the key findings.
In their paper, Paul and Arjun describe their work as representing a first step in establishing a symptom-based tool, and in this podcast, they detail what the next steps are, including how the tool could be used to measure a variety of other treatments. Paul and Arjun also comment on how this tool could be applied to extracting response trajectories to Cognitive Behavioural Therapy (CBT).
Paul and Arjun then turn to the translational opportunities for their research, including how they envisage their research being translated and what the implications of their findings are for CAMH professionals.
In this podcast, we are joined by Dr. Paul Croarkin of the Department of Psychiatry and Psychology at the Mayo Clinic Rochester, Minnesota, and Dr. Arjun Athreya of the Department of Molecular Pharmacology and Experimental Therapeutics at the same institution.
The focus of this podcast is on the JCPP paper ‘Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants’ (doi.org/10.1111/jcpp.13580).
Paul and Arjun set the scene by detailing what they looked at in this study, providing us with a summary of the paper, plus sharing insights into the methodology used for the research, before turning to the key findings.
In their paper, Paul and Arjun describe their work as representing a first step in establishing a symptom-based tool, and in this podcast, they detail what the next steps are, including how the tool could be used to measure a variety of other treatments. Paul and Arjun also comment on how this tool could be applied to extracting response trajectories to Cognitive Behavioural Therapy (CBT).
Paul and Arjun then turn to the translational opportunities for their research, including how they envisage their research being translated and what the implications of their findings are for CAMH professionals.