Healthcare Analytics Made Simple

▶Book Description
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.

▶What You Will Learn
⦁ Gain valuable insight into healthcare incentives, finances, and legislation
⦁ Discover the connection between machine learning and healthcare processes
⦁ Use SQL and Python to analyze data
⦁ Measure healthcare quality and provider performance
⦁ Identify features and attributes to build successful healthcare models
⦁ Build predictive models using real-world healthcare data
⦁ Become an expert in predictive modeling with structured clinical data
⦁ See what lies ahead for healthcare analytics

▶Key Features
⦁ Perform healthcare analytics with Python and SQL
⦁ Build predictive models on real healthcare data with pandas and scikit-learn
⦁ Use analytics to improve healthcare performance

▶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, although 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 in 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.