While an increasing amount of data is being produced by various biomedical and healthcare systems, they have not yet fully capitalized on the transformative opportunities that these data provide. Applying analytical techniques to big data can be of great benefit in the biomedical and healthcare domain, allowing identification and extraction of relevant information and reducing the time spent by biomedical and healthcare professionals and researchers who are trying to find meaningful patterns and new threads of knowledge.

To exploit these data for discovering new knowledge that can be translated into healthcare applications, there are fundamental data analysis difficulties that have to be overcome. Practical issues, such as handling noisy and incomplete data, processing compute-intensive tasks, integrating various data sources, and advanced and sophisticated data analytical techniques to exploit and manage these data, are challenges faced by biomedical informatics in the big data era.

The major goal of this special issue is to bring together the researchers in healthcare and data mining to illustrate pressing needs, demonstrate challenging research issues, and showcase the state-of-the-art research and development.

Potential topics include but are not limited to the following:

• Classifying and clustering biomedical and healthcare data
• Information extraction from biomedical and clinical corpora (full texts, abstracts, EHRs, clinical trials, etc.)
• Data preprocessing and cleansing to deal with noise and missing data in large biomedical or population health data sets
• Text mining for biomedical and healthcare data
• Algorithms to speed up the analysis of big biomedical and healthcare data
• Novel visualization techniques to facilitate the query and analysis of biomedical and healthcare data
• Exploitation of big data in healthcare
• Data mining for clinical decision support
• Data mining for clinical guideline/healthcare process improvement
• Application of data mining models for improving healthcare