Social networking with a deep learning perspective has emerged as a promising paradigm in medical informatics that can help players in this field get a competitive advantage in terms of enhanced patient satisfaction. Capturing, cleaning, and analyzing complex and large-scale patient-generated text streams is a difficult undertaking using standard tools and procedures. Many individuals utilize various social networking sites to discuss their healthcare experiences with diseases, treatments, companies, physicians, therapists, and so on. The information gathered from medical and healthcare professionals and patients is critical in providing the necessary information for pharmacovigilance in the case of adverse medication reactions. Similarly, such data, supplemented by a pharmacogenomics knowledge base, can aid in the process of pharmacogenomics, which tries to understand how people react differently to pharmaceuticals based on genetic characteristics.

Many reputable firms have been gathering data on their goods from their healthcare clients through the creation and maintenance of online profiles. However, there is a lack of automated systems and smart interfaces for acquiring, pre-processing, and analyzing data, as well as transforming it into useful information by taking remedial steps in terms of discovered gaps through consumer feedback for postmarketing monitoring. As a result, within the umbrella of health and medical informatics, there is a need to deploy social network-driven deep learning algorithms and construct automated solutions. In light of social networking philosophy and technology, this might help the healthcare business and associated stakeholders (patients, physicians, etc.). Information obtained through such strategies might also help lawmakers in healthcare departments gain a better understanding of the public's wants and requirements.

This Special Issue on social networking with a deep learning perspective for medical informatics aims to bring academia and industry together to address challenges and provide solutions for the development of practical applications for social networking-driven medical and healthcare informatics models, as well as devise new techniques for data acquisition, filtering, classification, and generation. This Special Issue will investigate this new dimension by giving comprehensive coverage of cutting-edge emergent concerns. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• Identifying consumer medical healthcare needs for medical items and equipment using deep learning techniques
• Creating a machine-readable corpus of adverse medication responses and illnesses as a benchmark
• Pharmacovigilance: using machine and deep learning techniques to monitor adverse drug reactions
• Researching pharmacogenomics using machine and deep learning approaches
• Detecting and extracting adverse drug responses from social network-based healthcare data at an early stage
• Examining patient feedback on treatments to analyze efficacy
• In medical and healthcare informatics, developing machine and deep learning approaches for consumer tone monitoring (in comparison to competitors, services, physicians, nursing, businesses, and so on)
• Proposing machine and deep learning methods for medical informatics postmarketing surveillance
• Deep learning models based on ontologies for assessing patient testimonials
• Investigating social network feedback for drug misuse and addiction detection