Healthcare analytics is an interdisciplinary domain aiming to assist physicians using computational techniques and digital health data. Analyzing a vast amount of patient data is vital to infer the characteristics of a patient cohort. Pattern recognition offers essential tools for a wide variety of healthcare tasks, such as medical image processing and classification, risk prediction, disease progression, patient subtyping, and medical text classification. Such tasks pose numerous challenges for pattern recognition. The heterogeneous, high-dimensional, non-linear, temporal, and distributed nature of the patient data complicate the traditional techniques. Such challenges inspire the pattern recognition domain to explore new ideas to help solve specific problems of the healthcare domain.
The goal of the proposed workshop is to present some of the latest developments in pattern recognition for healthcare analytics. The scope of the workshop entails but is not limited to
predictive modeling for heterogeneous patient data,
disease progression modeling for temporal patient data,
embedding learning for clinical notes,
medical image classification,
clustering for patient subtyping,
patient similarity learning for personalized medicine,
knowledge graph embedding for healthcare,
interpretable models and interactive tools for clinical decision support.