Machine Learning and Artificial Intelligence for Health Applications on Social Networks

Dear Colleagues,

The fast expansion of social media and social networks in the last few years is making available an enormous and continuous stream of user-generated contents containing invaluable information that can be used to understand, in near real time, human life dynamics worldwide. These massive quantities of data could support a wide range of medical and healthcare applications, including, among others, clinical trials and decision support, disease surveillance, personalized medicines, and population health management. Besides, social media combines textual, temporal, geographical, and network data, opening up unique opportunities to study the interplay between human mobility, social structure and disease transmission.

In this context, Artificial intelligence (AI) and machine learning techniques can play a crucial role to deal with such amount of heterogeneous, multi-scale and multi-modal data. Some examples of techniques that are gaining attention in this domain include deep learning, domain adaptation, semi-supervised approach, time series analysis, and active learning. Even though the use of machine learning and the development of ad-hoc techniques are gaining increasing popularity in the health domain, we can witness that a significant lack of interaction between domain experts and machine learning researchers still exists.

AI is changing the landscape of healthcare and modern personalized precision medicine. With the increasing availability of healthcare data and rapid progress of machine learning algorithms and analysis techniques, AI is gradually enabling doctors with tools for better diagnosis, disease surveillance, facilitating early detection, uncovering novel treatments, and creating an era of truly personalized medicine. In particular, novel approaches to AI and big data analytics and mining methods on social media data and social networks may provide invaluable means for health monitoring, surveillance, disease spreading, and outbreak prediction.

Artificial intelligence in healthcare is going play a significant role in solving the issues like drug-interaction, false alarms, over-diagnosis, and over-treatment. Moreover, AI with new technologies of IoT and Blockchain has tremendous scope for better medical treatment with data security.

The main areas of AI applications in healthcare are: providing personalized precision medicine, analysis and interpretation of radiology images, automated diagnosis, prescription preparation, clinical workflow monitoring, patient monitoring and care, discovery of new drugs, predicting the impact of gene edits, treatment protocol development, and early diagnoses of diseases.

The Special Issue solicits empirical, experimental, methodological, and theoretical research reporting original and unpublished results on social networks analysis and mining on topics in the realm of healthcare and health informatics along with applications to real life situations.

Dr. Carmela Comito
Guest Editor

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