Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Yogish, Deepa; Bharat, Suchak Pratik; Reddy, Shashank; Ramachandran, Siddharth; Yagnik, Pihoo Title A name given to the resource Transforming Teletherapy: Using Transfer Learning and NLP for Improved Mental Health Care Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Healthcare 5.0: AI Driven Workspace in Sustainable Telehealth;pp.61-81 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-09582-4_3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-09582-4_3</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032061703?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032061703?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Yogish D., Department of CSE, School of Engineering and Technology, Christ University, Bengaluru, India; Bharat S.P., Department of CSE (AIML), School of Engineering and Technology, Christ University, Bengaluru, India; Reddy S., Department of CSE (AIML), School of Engineering and Technology, Christ University, Bengaluru, India; Ramachandran S., Department of CSE (AIML), School of Engineering and Technology, Christ University, Bengaluru, India; Yagnik P., Department of CSE (AIML), School of Engineering and Technology, Christ University, Bengaluru, India Description An account of the resource The increasing reliance on tele-therapy for mental health support highlights the need for advanced methodologies to improve diagnostic precision and patient outcomes. This study explores the transformative potential of transfer learning in natural language processing (NLP) to enhance the detection of mental health conditions during tele-therapy sessions. Leveraging a dataset sourced from mental health-related subreddits, which includes conversations mapped to five target categories (Stress, Depression, Bipolar Disorder, Personality Disorder, and Anxiety), we fine-tuned a pre-trained BERT model for multi-class classification. Our study's results highlight significant performance enhancements achieved through the implementation of transformer-based models. The proposed framework achieved an accuracy of 83%, with macro average precision, recall, and F1-score values of 0.84, 0.83, and 0.83, respectively. Class-specific analysis further underscores the model's robustness, with precision ranging from 0.75 to 0.92 and recall values exceeding 0.80 for most categories. These outcomes significantly outperform traditional machine learning models such as Random Forest (accuracy: 72.65%) and Support Vector Machines (accuracy: 69.71%), demonstrating the superior capacity of BERT to capture complex linguistic patterns and semantic nuances in patient interactions. This research underscores the transformative role of transfer learning in NLP applications for tele-therapy, offering a scalable and precise solution for mental health assessment and paving the way for personalized, AI-driven interventions. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource BERT; Mental health; Natural Language Processing (NLP); Tele-Therapy; Transfer Learning; Transformer-based Models Publisher An entity responsible for making the resource available Springer Nature Relation A related resource ISBN: 978-303209582-4; 978-303209581-7; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online