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Healthcare Analytics Made Simple

Healthcare Analytics Made Simple

By : Kumar, Khader
4.4 (8)
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Healthcare Analytics Made Simple

Healthcare Analytics Made Simple

4.4 (8)
By: Kumar, Khader

Overview of this book

In recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising doctors and data scientists. It equips the data scientists’ work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes. This book is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed. By the end of this book, you will understand how to use Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples.
Table of Contents (11 chapters)
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References

Basole RC, Kumar V, Braunstein ML, et al. (2015). Analyzing and Visualizing Clinical Pathway Adherence in the Emergency Department. Nashville, TN: INFORMS Healthcare Conference, July 29-31, 2015.

Baxt, WG (1990). "Use of an Artificial Neural Network for Data Analysis in Clinical Decision-Making: The Diagnosis of Acute Coronary Occlusion." Neural Computation 2 (4): 480-489.

Ledley RS, Lusted LB (1959). "Reasoning Foundations of Medical Diagnosis." Science 130 (3366): 9-21.

Miller RA, Pople Jr. HE, Myers JD (1982). "INTERNIST-1, An Experimental Computer-Based Diagnostic Consultant for General Internal Medicine." New Engl J Med 307: 468-476.

Minsky M, Papert SA ( 1969). "Perceptrons." Cambridge, MA: The MIT Press.

Rumelhart DE, Hinton GE, Williams RJ (1986). "Learning representations by back-propagating errors." Nature 323(9): 533-536.

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