David Madigan sends the following. It looks like a really interesting place to submit papers for both statisticians and data scientists, so submit away!
Statistical Analysis and Data Mining, An American Statistical Association Journal
Call for Papers
Special Issue on Observational Healthcare Data
Guest Editors: Patrick Ryan, J&J and Marc Suchard, UCLA
Due date: July 1, 2013
Data sciences is the rapidly evolving field that integrates
mathematical and statistical knowledge, software engineering and large-scale data management skills, and domain expertise to tackle difficult problems that typically cannot be solved by any one discipline alone. Some of the most difficult, and arguably most important, problems exist in healthcare. Knowledge about human biology has exponentially advanced in the past two decades with exciting progress in genetics, biophysics, and pharmacology. However, substantial opportunities exist to extend the evidence base about human disease, patient health and effects of medical interventions and translate knowledge into actions that can directly impact clinical care. The emerging availability of ‘big data’ in healthcare, ranging from prospective research with aggregated genomics and clinical trials to observational data from administrative claims and electronic health records through social media, offer unprecedented opportunities for data scientists to contribute to advancing healthcare through the development, evaluation, and application of novel analytical solutions to explore these data to generate evidence at both the patient and population level. Statistical and computational challenges abound and
methodological progress will draw on fields such as data mining,
epidemiology, medical informatics, and biostatistics to name but a
few. This special issue of Statistical Analysis and Data Mining seeks to capture the current state of the art in healthcare data sciences. We welcome contributions that focus on methodology for healthcare data and original research that demonstrates the application of data sciences to problems in public health.
http://onlinelibrary.wiley.
com/journal/10.1002/(ISSN) 1932-1872