Ramin Safa

Ramin Safa, S. A. Edalatpanah, Ali Sorourkhah

Mental disorders such as depression and suicidal ideation are hazardous, affecting more than 300 million people over the world. However, on social media, mental disorder symptoms can be observed, and automated approaches are increasingly capable of detecting them. The considerable number of social media users and the tremendous quantity of user-generated data on social platforms provide a unique opportunity for researchers to distinguish patterns that correlate with mental status. This research offers a roadmap for analysis, where mental state detection can be based on machine learning techniques. We describe the common approaches for predicting and identifying the disorder using user-generated content. This research is organized according to the data collection, feature extraction, and prediction algorithms. Furthermore, we review several recent studies conducted to explore different features of candidate profiles and their analytical methods. Following, we debate various aspects of the development of experimental auto-detection frameworks for identifying users who suffer from disorders, and we conclude with a discussion of future trends. The introduced methods can help complement screening procedures, identify at-risk people through social media monitoring on a large scale, and make disorders easier to treat in the future.

Alireza Pourkeyvan, Ramin Safa, Ali Sorourkhah

Early diagnosis of mental disorders and intervention can facilitate the prevention of severe injuries and the improvement of treatment results. Using social media and pre-trained language models, this study explores how user-generated data can be used to predict mental disorder symptoms. Our study compares four different BERT models of Hugging Face with standard machine learning techniques used in automatic depression diagnosis in recent literature. The results show that new models outperform the previous approach with an accuracy rate of up to 97%. Analyzing the results while complementing past findings, we find that even tiny amounts of data (like users' bio descriptions) have the potential to predict mental disorders. We conclude that social media data is an excellent source of mental health screening, and pre-trained models can effectively automate this critical task.

Fatemeh Ebrahimzadeh, Ramin Safa

The concept of Metaverse has attracted a lot of attention in various fields and one of its important applications is health and treatment. The Metaverse has enormous potential to transform healthcare by changing patient care, medical education, and the way teaching/learning and research are done. The purpose of this research is to provide an introduction to the basic concepts and fundamental technologies of the Metaverse. This paper examines the pros and cons of the Metaverse in healthcare context and analyzes its potential from the technology and AI perspective. In particular, the role of machine learning methods is discussed; We will explain how machine learning algorithms can be applied to the Metaverse generated data to gain better insights in healthcare applications. Additionally, we examine the future visions of the Metaverse in health delivery, by examining emerging technologies such as blockchain and also addressing privacy concerns. The findings of this study contribute to a deeper understanding of the applications of Metaverse in healthcare and its potential to revolutionize the delivery of medical services.