Aim and Scope 

The fourth PRHA workshop aims to continue showcasing the latest developments in pattern recognition for healthcare analytics. As an interdisciplinary domain, healthcare analytics comprises a wide range of data-driven techniques proposed for digital health data. Particularly, deep learning and machine learning frameworks are vital for various healthcare tasks, including risk prediction, disease progression modeling, subtyping and phenotyping, and medical image and text recognition. The scope of the workshop entails but is not limited to 

• Bioinformatics

• Phenotyping and subtyping

• Patient monitoring and machine learning in pervasive healthcare

• Temporal modeling for disease progression

• Interpretable models for clinical decision support

• Privacy-preserving techniques for distributed and sensitive patient data

• Medical image analysis

Healthcare analytics tasks constitute various challenges due to the complex nature of digital patient data. Traditional deep learning and machine learning techniques do not inherently address the challenges in patient data that are often heterogeneous, distributed, non-linear, and temporal. Furthermore, interpretability and explainability have become inevitable requirements to consider while designing models to address bioinformatics and biomedical informatics tasks. In summary, the proposed workshop aims to showcase original ideas on predictive modeling, clustering, feature extraction, temporal analysis, data visualization, and interpretability for patient data in tabular, text, and image formats.

Important Dates

• Submission: May 15, 2026

• Notification: June 15, 2026

• Camera-ready: June 30, 2026

• Workshop: August 22, 2026