Data Analytics in Biomedical Engineering and Healthcare

Explores key applications using data analytics, machine learning, and deep learning in the health sciences and biomedical data fields

Kun Chang Lee (Edited by), Sanjiban Sekhar Roy (Edited by), Pijush Samui (Edited by), Vijay Kumar (Edited by)

9780128193143, Elsevier Science

Paperback, published 16 October 2020

292 pages
23.4 x 19 x 1.9 cm, 0.7 kg

Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks.

1. Data analytics applications in biomedical data 2. Predictive Health Analysis 3. Exploration of EHR (Electronic Health Records) using data science 4. Machine Learning and Deep Learning application on medical image analysis 5. Developing Clinical Decision Support System 6. Innovative Classification, Regression Model for predicting various diseases 7. Computational Drug Discovery using State of the Art Unsupervised learning 8. Genome Structure prediction using Predictive modelling 9. Hybrid learning for better medical diagnosis 10. Big data application in healthcare under MapReduce and Hadoop frameworks

Subject Areas: Biomedical engineering [MQW]