- Submission Deadline (EXTENDED): Apr 20, 2020
- Notification Due (EXTENDED): Apr 30, 2020
- Final Version Due: May 10, 2020
- CFP: on EasyChair and WikiCFP
The BOOM 2020 Workshop solicits the following submissions.
- Full papers that describe original research work that have not been published before, which will be published in a special issue of a partner journal (to be announced soon).
- Full papers from past BOOM have been published in special issues of IISE Transactions on Healthcare Systems Engineering, 2018; EURASIP Journal on Advances in Signal Processing (JASP), 2017; and EURASIP Journal on Bioinformatics and Systems Biology (JBSB), 2016.
- Full paper authors are also highly encouraged to submit short abstracts simultaneously through email to: ijcai1boom <AT> gmail <DOT> com for the consideration of workshop presentations.
- Short abstracts that either highlight significant works that have been published or accepted recently or report unpublished research findings, which will be included in workshop online proceedings (unarchived). Please format short abstracts according to IJCAI latex & word templates, with the page limit of 2 pages including references; and submit through email to: ijcai1boom <AT> gmail <DOT> com.
- Please NOTE the different submission forms of Full Papers and Short Abstracts!!
Both full and short submissions will be considered for oral and poster presentations at BOOM. The decision on presentation format for accepted submissions will be based primarily on an assessment of breadth of interest, and the construction of balanced and topically coherent sessions, while full papers will be given some priority for oral presentations.
Following past BOOM, we will continue to give out two best paper awards (long and short).
Topics of Interests
We encourage submissions from, but not limited to, the following inter-linked areas:
Category I: Machine Learning and Optimization Algorithms
- Applying cutting-edge machine learning (e.g., deep learning) and optimization techniques to tackle real-world medical and healthcare problems.
- Addressing challenges and roadblocks in biomedical informatics with reference to the data-driven machine learning, such as imbalanced dataset, weakly-structured or unstructured data, noisy and ambiguous labeling, and more.
- Designing novel, applicable numerical optimization algorithms for biomedical data, that is usually large-scale, high-dimensional, heterogeneous, and noisy.
- Re-visiting traditional machine learning topics such as clustering, classification, regression and dimension reduction, that find application values in newly-emerging biomedical informatic problems.
- Other closely-related disciplines, such as image processing, data mining, new computing technologies and paradigms (e.g., cloud computing), control theory, and system engineering.
Category II: Biomedical Informatics Applications
- Computational Biology, including the advanced interpretation of critical biological findings, using databases and cutting-edge computational infrastructure.
- Clinical Informatics, including the scenarios of using computation and data for health care, spanning medicine, dentistry, nursing, pharmacy, and allied health.
- Public Health Informatics, including the studies of patients and populations to improve the public health system and to elucidate epidemiology.
- mHealth Applications, including the use of mobile apps and wearable sensors for health management and wellness promotion.
- Cyber-Informatics Applications, including the use of social media data mining and natural language processing for clinical insight discovery and medical decision making.
We encourage papers with important new insights and experiences at the intersection of machine learning, optimization and bioinformatics. Those contributions should shed light on at least one topic mentioned above, while the above topics have obvious overlaps. For topics in Category I, we invite both theoretically novel and application-driven papers. For those in Category II, the idea is to keep the interested application domain focused yet broad, echoing multiple scales, ranging from molecules, individuals, to populations.