The study aimed to identify behavioral patterns based on cognitive behavioral therapy patients offered over the Internet.
The article entitled: A Machine Learning Approach to Understanding Patterns of Engagement With Internet-Delivered Mental Health Interventions was published on July 17 by JAMA Network Open. The purpose of the study was to examine how different types of patient behaviors manifest in the way they engage in internet-based cognitive behavioral therapy (iCBT), for symptoms of depression and anxiety.
For the study, 54,604 unidentified adult patients from the Space From Depression and Anxiety program were obtained between January 2015 and March 2019. They were obtained to create probabilistic models using machine learning techniques to learn different subtypes of patients, and their interaction with iCBT. machine learning to learn about different subtypes of patients, and their interaction with iCBT.
In addition, a physician-supported iCBT-based program was used that follows clinical guidelines for the treatment of depression and anxiety, adapted on a 2.0 web platform.
You can view the results and conclusions of this research through the following link: https://es.jamanetwork.com/journals/jamanetworkopen/fullarticle/2768347To learn more about articles and publications regarding Digital Health visit our Section of Referential Frameworks: https://saluddigital.com/marcos-referenciales/
