Aim and Scope
Harnessing the wealth of data in electronic health records has become a ubiquitous research topic over the last ten years and more recently has been the focus of large research initiatives across the globe. The aim of these initiatives is vast, ranging from enhancing our understanding of disease processes, improving the identification of patient cohorts for clinical research, characterising patterns of utilisation of treatment options, modelling and monitoring healthcare delivery, supporting population-wide healthcare management, and streamlining the translation of research findings into clinical practice and individualised patient care. The success of these challenges is underpinned by the combination of clinical, medical and epidemiological knowledge with advanced computational methods, highlighting the importance of the latter in achieving these ambitious aims. It would be impossible to imagine the tremendous progress made to date without the contribution made by areas such as pattern recognition, data mining, AI planning, statistical computing, databases, distributed and high performance computing, natural language processing, and privacy and security, which support the work of scientists in improving healthcare. Conversely, healthcare has presented one the most challenging testing grounds for computer scientists and mathematicians alike.
Topics Covered
This special issue of Computers in Biology and Medicine aims to provide an international forum for reviewing and sharing recent advances in computational methods that enable clinical and translational research using electronic health record data. In this issue research articles, case studies and systematic reviews are invited in the following areas:
Machine learning approaches for characterising patient cohorts
Computational methods for automatic data quality assurance
Approaches for record linkage and querying linked datasets
Modelling and simulation of healthcare systems
AI planning in healthcare
Modelling and simulation of disease processes for research and clinical applications
Computational methods in clinical and translational research
Healthcare knowledge management and decision support
Data mining and machine learning approaches to analyse EHR data
Natural Language Processing and Text Mining applied to EHR data
Schedule
Submission deadline: 30 April 2014
Reviews to authors: 30 June 2014
Revised submissions: 31 August 2014
Reviews to authors: 30 September 2014
Final papers: 30 November 2014
Guest Editor
Dr. Dionisio Acosta
Senior Research Associate, CHIME, University College London
Email: d.acosta@ucl.ac.uk