Preface
The functional aim of this book is to demonstrate how Python packages are used for 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, with the help of real-world examples.
Who this book is for
Healthcare Analytics Made Simple is for you if you are a developer who has a working knowledge of Python or a related programming language, even if you are new to healthcare or predictive modeling with healthcare data. Clinicians interested in analytics and healthcare computing will also benefit from this book. This book can also serve as a textbook for students enrolled on an introductory course on machine learning for healthcare.
What this book covers
Chapter 1, Introduction to Healthcare Analytics, provides a definition of healthcare analytics, lists some foundational topics, provides a history of the subject, gives some examples of healthcare analytics in action, and includes download, installation, and basic usage instructions for the software in this book.
Chapter 2, Healthcare Foundations, consists of an overview of how healthcare is structured and delivered in the US, provides a background on legislation that's relevant to healthcare analytics, describes clinical patient data and clinical coding systems, and provides a breakdown of healthcare analytics.
Chapter 3, Machine Learning Foundations, describes some of the model frameworks used for medical decision making and describes the machine learning pipeline, from data import to model evaluation.
Chapter 4, Computing Foundations – Databases, provides an introduction to the SQL language and demonstrates the use of SQL in healthcare with a healthcare predictive analytics example.
Chapter 5, Computing Foundations – Introduction to Python, gives a basic overview of Python and the libraries that are important for performing analytics. We discuss variable types, data structures, functions, and modules in Python. We also give an introduction to the pandas and scikit-learn libraries.
Chapter 6, Measuring Healthcare Quality, describes the measures used in healthcare performance, gives an overview of value-based programs in the US, and demonstrates how to download and analyze provider-based data in Python.
Chapter 7, Making Predictive Models in Healthcare, describes the information contained in a publicly available clinical dataset, including downloading instructions. We then demonstrate how to make predictive models with this data, using Python, pandas, and scikit-learn.
Chapter 8, Healthcare Predictive Models – A Review, reviews some of the current progress being made in healthcare predictive analytics for select diseases and application areas by comparing machine learning results to those obtained by using traditional methods.
Chapter 9, The Future – Healthcare and Emerging Technologies, discusses some of the advances being made in healthcare analytics through using the internet, introduces the reader to deep learning techniques in healthcare, and states some of the challenges and limitations facing healthcare analytics.
To get the most out of this book
Helpful things to know include the following:
- High school math, such as basic probability, statistics, and algebra
- Basic familiarity with a programming language and/or basic programming concepts
- Basic familiarity with healthcare and a working knowledge of some clinical terminology
Please follow the instructions in Chapter 1, Introduction to Healthcare Analytics for setting up Anaconda and SQLite.
Download the example code files
You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.
You can download the code files by following these steps:
- Log in or register at www.packtpub.com.
- Select the
SUPPORTtab. - Click on
Code Downloads & Errata. - Enter the name of the book in the
Searchbox and follow the onscreen instructions.
Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:
- WinRAR/7-Zip for Windows
- Zipeg/iZip/UnRarX for Mac
- 7-Zip/PeaZip for Linux
The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Healthcare-Analytics-Made-Simple. In case there's an update to the code, it will be updated on the existing GitHub repository.
We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
Download the color images
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/HealthcareAnalyticsMadeSimple_ColorImages.pdf.
Conventions used
There are a number of text conventions used throughout this book.
CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system."
A block of code is set as follows:
string_1 = '1' string_2 = '2' string_sum = string_1 + string_2 print(string_sum)
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
test_split_string = 'Jones,Bill,49,Atlanta,GA,12345'
output = test_split_string.split(',')
print(output)
Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Select System info from the Administration panel."
Note
Warnings or important notes appear like this.
Note
Tips and tricks appear like this.
Get in touch
Feedback from our readers is always welcome.
General feedback: Email [email protected] and mention the book title in the subject of your message. If you have questions about any aspect of this book, please email us at [email protected].
Errata: Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you have found a mistake in this book, we would be grateful if you would report this to us. Please visit www.packtpub.com/submit-errata, selecting your book, clicking on the Errata Submission Form link, and entering the details.
Piracy: If you come across any illegal copies of our works in any form on the Internet, we would be grateful if you would provide us with the location address or website name. Please contact us at [email protected] with a link to the material.
If you are interested in becoming an author: If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, please visit authors.packtpub.com.
Reviews
Please leave a review. Once you have read and used this book, why not leave a review on the site that you purchased it from? Potential readers can then see and use your unbiased opinion to make purchase decisions, we at Packt can understand what you think about our products, and our authors can see your feedback on their book. Thank you!
For more information about Packt, please visit packtpub.com.
