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You're reading from Healthcare Analytics Made Simple Techniques in healthcare computing using machine learning and Python

Product type Paperback

Published in Jul 2018

Publisher Packt

ISBN-13 9781787286702

Length 268 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Data Analysis

Authors (2):

Vikas (Vik) Kumar

Shameer Khader

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Table of Contents (11) Chapters

Preface

1. Introduction to Healthcare Analytics FREE CHAPTER

2. Healthcare Foundations

3. Machine Learning Foundations

4. Computing Foundations – Databases

5. Computing Foundations – Introduction to Python

6. Measuring Healthcare Quality

7. Making Predictive Models in Healthcare

8. Healthcare Predictive Models – A Review

9. The Future – Healthcare and Emerging Technologies

10. Other Books You May Enjoy

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Healthcare Foundations

This chapter is mainly aimed at developers who have limited experience of healthcare. By the end of it, you will be able to describe basic characteristics of healthcare delivery in the United States, you will be familiar with specific legislation in the US that is relevant to analytics, you will understand how data in healthcare is structured, organized, and coded, and you will be aware of frameworks for thinking about analytics in healthcare.

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Authors (2)

Khader

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Kumar

Ashish Kumar is a seasoned data science professional, a publisher author and a thought leader in the field of data science and machine learning. An IIT Madras graduate and a Young India Fellow, he has around 7 years of experience in implementing and deploying data science and machine learning solutions for challenging industry problems in both hands-on and leadership roles. Natural Language Procession, IoT Analytics, R Shiny product development, Ensemble ML methods etc. are his core areas of expertise. He is fluent in Python and R and teaches a popular ML course at Simplilearn. When not crunching data, Ashish sneaks off to the next hip beach around and enjoys the company of his Kindle. He also trains and mentors data science aspirants and fledgling start-ups.

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