Enhancing Predictive Power: Integrating a Linear Support Vector Classifier with Logistic Regression for Patient Outcome Prognosis in Virtual Reality Therapy for Treatment-Resistant Schizophrenia.

ABSTRACT: (1) Background: Approximately 30% of schizophrenia patients are known to be treatment-resistant. For these cases, more personalized approaches must be developed. Virtual reality therapeutic approaches such as avatar therapy (AT) are currently undergoing investigations to address these patients' needs. To further tailor the therapeutic trajectory of patients presenting with this complex presentation of schizophrenia, quantitative insight about the therapeutic process is warranted. The aim of the study is to combine a classification model with a regression model with the aim of predicting the therapeutic outcomes of patients based on the interactions taking place during their first immersive session of virtual reality therapy. (2) Methods: A combination of a Linear Support Vector Classifier

SUBMITTER: Hudon A 

PROVIDER: S-EPMC10744538 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature