Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010371640 | R859.7.A78 S63 2020 | Open Access Book | Book | Searching... |
On Order
Summary
Summary
About the Book
The book provides details of applying intelligent mining techniques for extracting and pre-processing medical data from various sources, for application-based healthcare research. Moreover, different datasets are used, thereby exploring real-world case studies related to medical informatics. This book would provide insight to the learners about Machine Learning, Data Analytics, and Sustainable Computing.
Salient Features of the Book
Exhaustive coverage of Data Analysis using R Real-life healthcare models for: Visually Impaired Disease Diagnosis and Treatment options Applications of Big Data and Deep Learning in Healthcare Drug Discovery Complete guide to learn the knowledge discovery process, build versatile real life healthcare applications Compare and analyze recent healthcare technologies and trendsTarget Audience
This book is mainly targeted at researchers, undergraduate, postgraduate students, academicians, and scholars working in the area of data science and its application to health sciences. Also, the book is beneficial for engineers who are engaged in developing actual healthcare solutions.
Author Notes
Dr. Adwitiya Sinhareceived her PhD from Jawaharlal Nehru University (JNU), New Delhi. She is a recipient of a Senior Research Fellowship from CSIR, New Delhi, India and a UGC Research Scholarship. Her application-based research is mainly focused on large-scale graphs, data analytics, and confluence of sensor-based applications with social networking.
Megha Rathi has 10 years of teaching experience. She has worked on the Xform generator research project of at NIC, Delhi. She has experience in software development and worked as a Project Associate at IIT Delhi. Her research areas include Data Mining, Data Science Analytics, Health Science, and Machine Learning.
Reviews 1
Choice Review
In this practical volume on applications of data science in various health care scenarios, editors Sinha and Rathi compile a set of papers that, collectively, provide a sweeping survey of how big data can address persistent challenges in the development and distribution of medical diagnosis and treatment. Individual chapters address a broad range of topics, including methods for classifying genetic mutations as pathogenic or benign and for staging cancers based on imaging data. In places, the volume could benefit from editing for concision, but carefully designed schematics, screenshots, and snippets of computer code ensure a clear overview even at points where the text is a bit cumbersome. The book is especially successful in providing a conceptual overview of how complex, emerging techniques (such as machine learning) are applied in practice. The editors describe their work as intended principally for researchers already working in medical informatics, but the volume is also promising as a textbook for courses designed to introduce advanced undergraduates to the range of ways that big data can be used to advance human health. Summing Up: Recommended. Lower- and upper-division undergraduates. Graduate students. Students enrolled in two-year technical programs. --Diane Patricia Genereux, Broad Institute of Harvard University and Massachusetts Institute of Technology
