Biomedical informatics as an emerging field has been fascinating talents from artificial intelligence and machine learning for its unique opportunities and challenges. Fast-growing biomedical and healthcare data have encompassed multiple scales ranging from molecules, cells, individuals, to populations and have connected various entities in healthcare systems such as providers, pharma, and payers with increasing bandwidth, depth, and resolution. These data are becoming an enabling resource to harness for scientific knowledge discovery and clinical decision making. Meanwhile, the sheer volume and complexity of the data present major barriers toward their translation into effective clinical actions. In particular, biomedical data often feature large volumes, high dimensions, imbalance between classes, heterogeneous sources, noises, incompleteness, and rich contexts, which challenges the direct and immediate success of existing machine learning and optimization methods. Therefore, there is a compelling demand for novel algorithms, including machine learning, data mining and optimization, that specifically tackle the unique challenges associated with biomedical and healthcare data and allow decision-makers and stakeholders to better interpret and exploit the data.
The special issue is in tandem with the 4th BOOM (Biomedical infOrmatics with Optimization and Machine learning; http://ijcai-boom.org/) workshop that aims at catalyzing synergies among biomedical informatics, artificial intelligence, machine learning, and optimization. Without restrictions to the BOOM participants, we invite general submissions with important new theories, methods, applications, and insights at the intersection of artificial intelligence, machine learning, optimization, and biomedical informatics. The topics of interest include, but are not limited to, the following inter-linked ones:
Category I: Machine Learning and Optimization Algorithms
- Developing and applying cutting-edge machine learning (e.g., deep learning) and optimization (e.g., large-scale 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 values in newly-emerging biomedical informatic applications.
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.
Full papers for the special issue should be submitted by April 15, 2020.
Yang Shen, Texas A&M University, USA Zhangyang Wang, Texas A&M University, USA Shuai Huang, University of Washington, USA Jiayu Zhou, Michigan State University, USA Qing Ling, Sun Yat-Sen University, China, João Manuel R.S. Tavares, Universidade do Porto, Portugal