|000||03270 a2200205 4500|
|008||151110b2015 xxu||||| |||| 00| 0 eng d|
|245||_aHealthcare data analytics|
|300||_axxviii, 724 p.|
_aChapman & Hall/CRC data mining and knowledge discovery series
|504||_aTable of contents: 1. An introduction to healthcare data analytics 2. Electronic health records : a survey 3. Biomedical image analysis 4. Mining of sensor data in healthcare : a survey 5. Biomedical signal analysis 6. Genomic data analysis for personalized medicine 7. Natural language processing and data mining for clinical text 8. Mining the biomedical literature 9. Social media analytics for healthcare 10. A review of clinical prediction models 11. Temporal data mining for healthcare data 12. Visual analytics for healthcare 13. Predictive models for integrating clinical and genomic data 14. Information retrieval for healthcare 15. Privacy-preserving data publishing methods in healthcare 16. Data analytics for pervasive health 17. Fraud detection in healthcare 18. Data analytics for pharmaceutical discoveries 19. Clinical decision support systems 20. Computer assisted medical image analysis systems 21. Mobile imaging and analytics for biomedical data|
|520||_aThe book details novel techniques for acquiring, handling, retrieving, and making best use of healthcare data. It analyzes recent developments in healthcare computing and discusses emerging technologies that can help improve the health and well-being of patients. Written by prominent researchers and experts working in the healthcare domain, the book sheds light on many of the computational challenges in the field of medical informatics. Each chapter in the book is structured as a "survey-style" article discussing the prominent research issues and the advances made on that research topic. The book is divided into three major categories: • Healthcare Data Sources and Basic Analytics - details the various healthcare data sources and analytical techniques used in the processing and analysis of such data • Advanced Data Analytics for Healthcare - covers advanced analytical methods, including clinical prediction models, temporal pattern mining methods, and visual analytics • Applications and Practical Systems for Healthcare - covers the applications of data analytics to pervasive healthcare, fraud detection, and drug discovery along with systems for medical imaging and decision support Computer scientists are usually not trained in domain-specific medical concepts, whereas medical practitioners and researchers have limited exposure to the data analytics area. The contents of this book will help to bring together these diverse communities by carefully and comprehensively discussing the most relevant contributions from each domain. (https://www.crcpress.com/Healthcare-Data-Analytics/Reddy-Aggarwal/9781482232110)|
_aStatistics as Topic
_aReddy, Chandan K.
_aAggarwal, Charu C.