Call for paper:

Precision medicine is one of the most revolutionary and promising advances in healthcare today transitioning from one-size-fits-all healthcare to personalized, data-driven treatment that enables improved patient outcomes. Precision or personalized medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response; and medical decisions are based on individual patient characteristics, environment, and lifestyle. Doctors and researchers can use precision medicine to predict more accurately which treatment and prevention strategies will work best for a particular patient. In other words, precision medicine offers a path to helping people recover from illness faster and stay healthy longer.

Precision medicine is deeply connected to and dependent on data science, specifically machine learning which have proven during recent years to be promising in predicting disease risk from available multidimensional clinical and biological data. Taking advantage of high-performance computer capabilities, machine learning algorithms can now achieve reasonable success in predicting risk in certain cancers and cardiovascular disease. The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize health care since sophisticated computation and inference techniques are used to generate insights that empowers clinician decision making. In this context, electronic health records (EHRs) offer great promise for accelerating the predictive analysis needed in precision medicine. In the last decade, predicting patients’ risk of developing certain diseases has become an important research topic in healthcare, being the accurate identification of the similarities among patients based on their historical records is a key step in personalized healthcare. In addition, explanations supporting the output of a ML model are crucial in precision medicine, where experts require far more information from the model than a simple binary prediction for supporting their diagnosis. Therefore, explainable or interpretable models, which fall within the eXplainable Artificial Intelligence (XAI) field, allow healthcare experts to make reasonable and data-driven decisions to provide more personalized and precise treatments.

Topics of interest include, but are not limited to, the following:

Workshop Organizers:

Sultan Turhan (, PhD., Department of Computer Engineering, Galatasaray University

Assist. Prof. Ozgun Pinarer (, Department of Computer Engineering, Galatasaray University

Pedro A. Moreno-Sanchez (, PhD., RDI Expert/Researcher - Seinäjoki University of Applied Sciences

Important Dates:

Paper submission deadline: May 11th, 2021 (Last Call)

Notification of acceptance: May 15th, 2021

Camera-ready copy due: May 21st, 2021

Submission Instructions:

Please submit a full-length paper (up to 8 page IEEE 2-column format) through the online submission system. Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines. Detailed instructions for the authors can be found at the conference website. All submissions will be published in IEEE Xplore and indexed in other Abstracting and Indexing (A&I) databases. Accepted papers have an oral presentation slot at the conference. All accepted papers must be presented by one of the author/s in the conference to include the article in the proceedings. 

For more information about the submission, please visit the conference web site: