Advances of Machine Learning for Knowledge Mining in Electronic Health Records by P. Mohamed Fathimal, T. Ganesh Kumar, J. B. Shajilin Loret
English | March 6, 2025 | ISBN: 1032526106 | 284 pages | MOBI | 13 Mb
English | March 6, 2025 | ISBN: 1032526106 | 284 pages | MOBI | 13 Mb
The book explores the application of cutting-edge machine learning and deep learning algorithms in mining Electronic Health Records (EHR). With the aim of improving patient health management, this book explains the structure of EHR consisting of demographics, medical history, and diagnosis, with a focus on the design and representation of structured, semi-structured, and unstructured data.
- Explains the design of organized, semi-structured, unstructured, and irregular time series data of electronic health records
- Covers information extraction, standards for meta-data, reuse of metadata for clinical research, and organized and unstructured data
- Discusses supervised and unsupervised learning in electronic health records
- Describes clustering and classification techniques for organized, semi- structured, and unstructured data from electronic health records
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