Evaluating the Efficacy of Deep Learning Models in Personalizing Treatment for Anxiety Disorders

The current research aims to explore the effectiveness of deep learning models in addressing the problem of treatment personalization for patients struggling with anxiety disorders. The latter have been associated with a wide array of challenges stemming from the varying treatment outcomes to a broad range of symptoms, and personalized treatment is viewed as vital in improving the patients’ prognoses. The research relies on the data of various patients, including those from controlled clinical trials and HER data, to evaluate the effectiveness of DL architectures such as Deep Reinforcement Learning (DRL) as well as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks. The approaches leverage preprocessing and training to minimize the number of treatment response prediction errors and accurately classify the level of anxiety. The results highlight the pressing urgency of developing a new treatment paradigm for anxiety disorders and question the effectiveness of the classic one-fits-all approach. Moreover, the research represents an attempt to bring Deep Learning (DL) algorithms to psychiatry and revolutionize mental health treatment through personalization. The outcomes support that DRL is the most effective at predicting the outcomes and properly classifying anxiety levels with few accurate measures. Overall, the findings confirm the effectiveness of DL models for personalized psychiatry and suggest further opportunities for leveraging them to improve the situation with mental health overall. It is evident that similar efforts can only be successful when theorists and practitioners join forces to achieve major changes, and by offering valuable survey results, the research paves the way to further meaningful dialogue.