IEEE International Conference on E-health Networking, Application & Services
17-19 October 2022 // Genoa, Italy


Workshop on Thick Data Analytics for Healthcare Applications - TDAEH 2022



Professor Sabah Mohammed, Computer Science Department at Lakehead University, Canada

Professor Simon James Fong, Adjunct Professor at Chongqing Technology and Business University, China

Professor Jinan Fiaidhi, Computer Science Department, Lakehead University, Canada

Professor Honghao Gao, Faculty of Computer Engineering and Science, Shanghai University, China



Detecting and analyzing patient insights from social media enables healthcare givers to better understand what patients want and also to identify their pain points. Healthcare institutions cannot neglect the need to monitor and analyze popular social media outlets such as Twitter and Facebook. However, healthcare givers need to be able to engage with their patients and adapt to their preferences and build convincing evidence based decisions. Experience shows the neither the sole physician-based decision nor the data-driven decision made through clinical pathways is enough to make the best-in-class care solutions. Social media and healthcare are a powerful combination. Social networks have become an important health and information resource as patients use it to access health-related information, share their medical stories and experiences with other patients, and communicate with healthcare providers for seeking healthcare supports. It opens a great opportunity for the development and application of artificial intelligence techniques and data mining approaches to study social media data for health-related issues and promoting healthcare. However, analyzing social media relying on simplistic textual analytics that use of big data technologies to learn and mine consumer/patient insights is no longer sufficient as most of these analytics utilize sort of bag-of-words counting algorithms. The majority of projects utilizing big data analytics have failed due to the obsession with metrics at the expense of capturing the customer's perspective data, as well as the failure in turning consumer insights into actions. Most of the consumer insights can be captured with qualitative research methods that work with small, even statistically insignificant, sample sizes. Employing qualitative analytics along with other quantitative analytics can provide powerful actionable intelligence which acquires understanding to broad questions about the consumer needs in tandem with analytical power. The start is to identify the relationships between constituents of the healthcare pain points as echoed by the social media conversations in terms of sociographic network where the elements composing these conversations are described as nodes and their interactions as links. In this part, conversation groups of nodes that are heavily connected will be identified representing what we call conversation communities. By identifying these conversation communities several consumer hidden insights can be inferred from using techniques such as visualizing conversation graphs relevant to given pain point, conversation learning from question answering, conversations summaries, conversation timelines, conversation anomalies and other conversation pattern learning techniques. These techniques will identify and learn the patient insights without forgetting from the context of conversation communities, are tagged as "thick data analytics". Additionally machine learning methods can be used as assistive techniques to learn from the identified thick data and build models around identified thick data. With the use of transfer learning we also can fine tune these models with the arrival of new conversations. This workshop aims at providing a collaborative platform for like-minded researchers from multi-disciplines (academia, clinicians, hospitals, service providers etc.) come together to share ideas, join forces and work together towards this existing direction of thick data research.


Scope of Papers

Including but not limited to the following:
•    Data analytics and Healthcare applications
•    Patient insights discovery
•    Graph based algorithms
•    Graph-based machine learning
•    Graph-based transfer learning
•    Explainable AI
•    Patient preferences and prognosis modelling
•    Thick data algorithms and technology
•    Conversation communities modelling
•    Social media analysis and mining
•    Thick data deep learning
•    SaaS and cloud-based medical service provision
•    SaaS and cloud-based thick data platforms


Important Dates

Paper Submission: 1 August 2022

Notification of Acceptance: 15 August 2022

Final Manuscript (Camera ready): 30 August 2022


Submission Guidelines

Submissions shall be done via EDAS system using this LINK.