Pattern Recognition for Bioinformatics and Digital Health
Pattern recognition has been offering essential tools for extracting meaningful information from biological and clinical data leading to frameworks that support clinical decision-making and research. In recent years, pattern recognition techniques are successfully applied to a wide variety of healthcare tasks, such as risk prediction, deciphering disease progression, patient subtyping, and medical text classification. Healthcare tasks pose numerous challenges for pattern recognition. The heterogeneous, high-dimensional, non-linear, temporal, and distributed nature of the patient and biological data complicate the traditional techniques. Such challenges inspire the pattern recognition domain to explore new ideas to solve specific challenges in the health domain.