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Big data analytics for healthcare
Abstract
Large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). Those data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. In this tutorial, we introduce the characteristics and related mining challenges on dealing with big medical data. Many of those insights come from medical informatics community, which is highly related to data mining but focuses on biomedical specifics. We survey various related papers from data mining venues as well as medical informatics venues to share with the audiences key problems and trends in healthcare analytics research, with different applications ranging from clinical text mining, predictive modeling, survival analysis, patient similarity, genetic data analysis, and public health. The tutorial will include several case studies dealing with some of the important healthcare applications.
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Citations
Cited by
Aceto, G., Persico, V., & Pescapé, A. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2018.02.008 · Cited by 504
A deep learning approach for ECG-based heartbeat classification for arrhythmia detection
Sannino, G., & De Pietro, G. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.03.057 · Cited by 445
Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities
Rehman, A., Naz, S., & Razzak, I. (2022) · Multimedia Systems · doi:10.1007/s00530-020-00736-8 · Cited by 264
Computational Health Informatics in the Big Data Age: A Survey
Fang, R., Pouyanfar, S., Yang, Y., Chen, S.-C., & Iyengar, S. S. (2017) · ACM Computing Surveys · doi:10.1145/2932707 · Cited by 210
Health Big Data Analytics: A Technology Survey
Harerimana, G., Jang, B., Kim, J. W., & Park, H. K. (2018) · IEEE Access · doi:10.1109/access.2018.2878254 · Cited by 119
Metadata
- Authors
- Sun, J., & Reddy, C. K.
- Venue
- Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2013-08-11
- Pages
- 1525-1525
- OpenAlex
- W2036356299
- Cited by
- 195
- References
- 0
- Type
- conference abstract
- Language
- en
- Open access
- closed
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