APS
31st APS Annual Convention
Applying Machine Learning and Related Technologies to Decode In-Session Emotional Processes and Personalize Treatment Recommendations
In this symposium, we will apply machine learning and related technologies to a) the personalization of treatment selection and b) the identification of in-session emotional processes. We will show how these models replicate or outperform clinical judgment and indicate how these technologies can be incorporated into clinical decision-making.
Chairs & Discussants
- Matthew SouthwardChair
The Ohio State University - Theodore BeauchaineDiscussant
The Ohio State University
Presentations
- Personalized Prediction of Antidepressant Versus Placebo Response: A Randomized Clinical TrialChristian Webb, Madhukar Trivedi, Zachary Cohen, Daniel Dillon, Jay Fournier, Maurizio Fava, et al.
- Using Natural Language Processing to Automatically Rate Emotion in PsychotherapyMichael Tanana, Christina Soma, Patty Kuo, Brian Pace, Zac Imel, David Atkins, et al.
- Computer-Vision and Machine Learning Can Classify Facial Expressions of Emotion with Accuracy That Rivals Human CodersNathaniel Haines, Matthew Southward, Jennifer Cheavens, Theodore Beauchaine, Woo-Young Ahn
