Manuscript Topics

The delivery of real-time data provides the opportunity to build knowledge, but data accumulates in a speed unmatchable by the human capacity of data processing. Thus, approximation of unknown functions from sampled data is an important activity in modern modeling and systems theory. It is important to develop models from data, which have sufficient generalization power and can describe the underlying process with accuracy, despite the nonlinearity and the complexity of these processes. These models are formulated usually using a mathematical language to describe the complex behavior of the system. Mathematical models are quantitative models and are often expressed in terms of statistical models, fuzzy logic models and empirical relationships.

Health care is one of the areas where data has been growing exponentially. Improving quality, safety or clinical effectiveness as well as reducing costs are nowadays the main concerns for health care decision-makers. These are challenging problems, and the structure or design of a system may influence the outcome. Ultimately, the development of personalized models, that can adapt to the specificities of the individual patient can deliver the necessary quality of care. In clinical decision support systems, it is crucial to interpret the developed models by determining which attributes are chosen by the artificial intelligence techniques and what is their clinical significance; propose alternative procedures or develop criteria for classifying patients into patient sub-group; and designing a post-implementation assessment of how well the system meets the goals.

Artificial intelligence can have an important role in health care as it can provide a transparent description of the system that reflects the nonlinearity of the system. Rule-based models, as e.g. fuzzy models, allow for a linguistic description of the knowledge captured in the model. It can also help the identification of important factors or features that identify specific groups of patients within a specific clinical setting. The design of specific decision models can support clinicians' decisions in terms of identifying the most suitable therapy for a specific patient, to achieve more favorable clinical outcomes and preventing poor outcomes due to practice variation.

This special issue focuses on a wide range of topics applying mathematical tools, such as quantitative, combinatorial, logical, algebraic and algorithmic methods to model and design complex health care systems, based on the mathematical foundations of artificial intelligence. The mathematical tools can be as diverse as decision support, automated deduction, reasoning, knowledge-based systems, machine learning, computer vision, etc. The special issue considers papers that develop and analyze mathematical models with concrete applications in health care based on artificial intelligence methodologies, and papers proposing new (mathematical) methods with a clear scientific and engineering motivation that result in an improved understanding of health care applications under study.

Instruction for Authors
http://www.aimspress.com/math/news/solo-detail/instructionsforauthors
Please submit your manuscript to online submission system
https://aimspress.jams.pub/