Biomedical Applications of Soft Computing
Alexandru Floares (Romania)
Liviu Ciortuz (Romania)
Medicine and its scientific and technological background are rapidly and profoundly changing in the Information Age. Our capacity to produce and record huge amount of complex biomedical data - patient conditions, diagnostic tests, treatments, outcomes, different kind of "omics" data (genomics/proteomics, etc.), biosignals and images – have dramatically increased. This data provides an unprecedented source of information that can lead to potential improvements in medical diagnostic, prognostic, and individualized, optimized treatment strategy. Although technology has brought about tremendous new sources of important biomedical data, we have not moved very far with regard to extracting the knowledge that lies latent in this data. In recent years, modern computer science has brought forth tremendous new tools such as artificial neural networks, decision trees, fuzzy logic, evolutionary computing, support vector machines, and the like. Also, the prevailing reductionist view by its own results seems to lead biomedicine to the systemic view. In some recent studies, omics data are placed in a pathways/networks context. Most often, only the structure of these networks is investigated, the dynamics being a more complicate problem, probably a challenge to the dynamical systems and control theories.