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  • Format: ePub

All stakeholders in the health paradigm, including payers, providers and vendors, agree that predictive analytics can help to increase the quality of health care, prevent adverse events, improve overall health, and decrease costs. AI and analytics books that provide a comprehensive coverage of the technical implementation details are available. However, there is a lack of books that present an application-oriented treatment of analytics in healthcare.
The purpose of this book is to present where and how analytics can be employed to improve healthcare. To achieve this purpose, the focus in
…mehr

Produktbeschreibung
All stakeholders in the health paradigm, including payers, providers and vendors, agree that predictive analytics can help to increase the quality of health care, prevent adverse events, improve overall health, and decrease costs. AI and analytics books that provide a comprehensive coverage of the technical implementation details are available. However, there is a lack of books that present an application-oriented treatment of analytics in healthcare.

The purpose of this book is to present where and how analytics can be employed to improve healthcare. To achieve this purpose, the focus in chapters is placed on reviewing and analysing the current and future applications of analytics in several health care disciplines, which can, later on, contribute to technical implementation.

This book is primarily for Medical Students, Physicians, Biomedical Engineers, Data Scientists, and Hospital Administrators. As such, it will be useful for offering a two-semester course on Predictive Analytics in Healthcare in Biomedical Engineering Departments, Medical Schools and even Business Schools.

The entire subject matter of the book makes it a useful knowledge resource for students aspiring to work in the healthcare industry and hospitals. The book will serve the need of the state of healthcare delivery as it progresses into a format wherein a combination of disease data and patient data is useful to make more accurate detection, diagnosis, and treatment decisions.


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Autorenporträt
Dr Vinithasree Subbhuraam has over 15 years of experience in biomedical data science and has utilized predictive analytics for designing clinical decision support systems to detect diseases such as carotid atherosclerosis, fatty liver, diabetes, epilepsy, and cancers in the thyroid, breast, ovaries, and prostate. Her work on breast cancer has been cited in World Health Organization Handbooks on Cancer Prevention. Dr Subbhuraam is also an experienced researcher and mentor, particularly adept at designing and developing digital health solutions for highly complex technical and scientific problems that directly impact global healthcare. She has over 95 publications in high-impact factor peer-reviewed international journals, conferences, and books.