The widespread adoption and popularity of health-related social media for patient-to-physician, patient-to-patient, and physician-to-patient communication provide opportunities to patients, in which hundreds of thousands of patients take the current technology to bring up serious health related events. One can see a large number of cancer patients are now using health-related social media to discuss and share their experiences in cancer related topics, including cancer diagnosis, risk factors, examination, prevention and treatment purposing to name a few. As social media continues to grow in size and complexity, it is going to be an extremely overwhelming task in the human scale to read and extract health information from social media posts, and there is thus a pressing need to implement efficient tool sets to automatically harness the wealth of social media data in an efficient and timely fashion. Tons of high quality information available on the social media along with smart computational methods, including artificial intelligence (AI), machine learning, deep learning, text analytics, and big data infrastructures promise the opportunity to build descriptive and predictive models to first turn those text data into precise facts and knowledge, and then improve individual and public health in cancer prevention, diagnosis, and treatment.
To promote cancer research, this special track mainly focuses on AI algorithms, solutions, and design strategies to address descriptive and predictive text analytics models over the generic as well as health-related social media (e.g., Twitter, Google+, WedMD, Patient, MedHelp, and DailyStrength). The track is expected to bridge the gap between AI, social media, and cancer research, opening innovative research avenues from the AI community to cancer study. The first special track on AI meets Social Media: Integrating Artificial Intelligence and Social Media to Advance Cancer Research is taking place on October 14-16, 2019 (In conjunction with ISMCO 2019, the First International Symposium on Mathematical and Computational Oncology) in Lake Tahoe, NV, USA . The track will consist of a combination of invited presentations, panel discussions, and paper presentations. We allocate significant time for open discussions on best practices and future directions in the research area. This is a new and emerging area for computational oncology community and we do hope this track will bring together scientists, researchers and interested audiences to explore the open problems, challenges, applications, and future directions in this vast progressing domain. Given the fact that cancer is the second cause of mortality worldwide and the leading cause of death in the United States, the proposed special track can be thus justified based on both international and national health concerns.
The topics of interests include the integration of AI, machine learning, deep learning, and text analytics and social media data to:
Papers submitted to the Special Track must not have been previously published and must not be currently under consideration for publication elsewhere. Manuscripts should be submitted in camera‐ready format and should not exceed 12 pages, including figures, tables, and references (see http://ismco.net/index.php/paper-submission for more details). All accepted papers will appear in the symposium proceedings which will be published by Springer‐Verlag in the Lecture Notes in Computer Science (LNCS) series.
Information on abstracting and indexing of the LNCS series can be found here. All full papers will also be submitted for indexing to PubMed Central. A “best paper award” will be sponsored by Springer-Verlag. Authors of selected papers (full or abstract) presented at ISMCO will be invited to submit extended versions of their papers for publication in a special journal issue (TBD).
TBD