The Power of Prediction in Health Care: A Step-by-step Guide to Data Science in Health Care: A Step-by-step Guide to Data Science in Health Care

By Rafiq Muhammad

Are you an aspiring data science student or early career researcher taking your first steps into data science? Are you overwhelmed and lost in the vast sea of information? This simplified data science guide is for you.

This book provides a step-by-step approach to how data science projects can be concep

• Language: English
• Publisher: Muhammad Rafiq
• Release date: Apr 17, 2024
• ISBN: 9789198900804

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The Power of Prediction in Health Care

A Step-by-step Guide to Data Science in Health Care

THE POWER OF PREDICTION IN HEALTH CARE

A Step-by-step Guide to Data Science in Health Care

Rafiq Muhammad, MD, MIHMEP, Ph.D.

Disclaimer:

No segment of this book might be replicated, disseminated, or transferred in any structure or using all means, comprising copying, recording, or mechanical or electronic techniques, or by any data stockpiling and recovery framework without the written consent of the author.

Notice of Liability:

The information provided in this book is provided without any warranty. The writer will not be liable to any individual or an entity with respect to any misfortunes or liabilities caused or asserted to be caused directly or indirectly by the content and the links provided in this book.

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The internet is a fluid medium, and the websites keep changing with time. The links provided in this book are for information purposes only, and the author does not give a warranty for any content, accuracy, or other intended purposes.

Imprint: Muhammad Rafiq

ISBN: 978-91-989008-0-4

©Copyright Rafiq Muhammad 2023, Protected by Copyright Law

Why I Wrote This Book?

As a freshly graduated medical doctor, it crossed my mind how nice it would be to have a computer program that could help in diagnosing complex patients based on their presenting signs and symptoms. That was the start of my journey to explore how computer models and data can be helpful in clinical practice for optimal clinical decision-making around complex patients. Instead of continuing the route to become a medical specialist, I decided to pursue my career in health care data science.

Having the passion, curiosity, and pursuit of learning, I have always been fascinated by the ideas of how data analytics and digitalization could transform health care. When I was exploring ideas for my Ph.D. project, the idea of exploring the potential of Artificial Intelligence and Machine Learning in health care got me fascinated, and I pursued and completed my Ph.D. in healthcare data science.

In the early days of my career and while pursuing my Ph.D. in data science, I often found myself overwhelmed and lost in the vast sea of information. As someone with no prior background in computer science, I could not help but feel intimidated by the complex world of data analysis and machine learning. However, as I embarked on this journey and persevered through the challenges, I not only completed my Ph.D. education but also gained valuable insights and experiences that can be immensely helpful to newcomers in the field. In this book, I aim to share an easy guide for those aspiring data science graduate students and early career researchers who are taking their first steps into the exciting and ever-evolving realm of data science.

This book provides a step-by-step approach to how data science projects can be conceptualized, designed, and developed in health care by aspiring data scientists. We will start on an educational journey that equips graduate students and early career researchers with hands-on knowledge and practical skills so they may fully realize the amazing potential of data science in the field of healthcare.

Since the advent of data science, we have access to a wide range of tools and methods that allow us to glean priceless insights from massive amounts of healthcare data, opening the door to more accurate forecasts and wise decision-making. However, navigating the complex data science world can be difficult, especially for individuals working in the healthcare industry who might have no background in it.

This book came into being because of my genuine belief that aspiring data scientists would benefit from a simplified and understandable guide that demystifies the complexities of data science. I felt compelled to fill this gap after seeing firsthand the difficulties experienced by many aspiring data scientists and healthcare professionals who want to use the potential of data science but are unsure of where to start or how to progress successfully. My main goal in writing the book is to give you a methodically designed, step-by-step strategy that clarifies the complicated world of data science so you may build a solid foundation and useful skills.

This book, in contrast to other resources, takes a practical approach, skillfully taking you through each step of the data science process with clarity and conciseness. You will find a real-world example in R that is geared exclusively to aspiring data scientists in healthcare industry. By meticulously following the step-by-step instructions and making the most of the plethora of resources at your disposal, you will develop the knowledge and skills required to start an exciting journey as a data scientist in healthcare.

I cordially invite you to go out on this thrilling adventure and embrace the enormous power of data science in the field of healthcare. Let us work together to unleash the POWER OF PREDICITON IN HEALTH CARE and leave an impression on the future of our great profession.

Unique Features and Structure of This Book

This book comprises nine chapters, organized as follows:

Chapter one introduces artificial intelligence and prospects of career in data science in healthcare.

Chapter two gives and overview of the fundamentals of data science in healthcare.

Chapter three gives an overview of the steps in data analysis and AI model development in healthcare.

Chapter four provides a comprehensive overview of the tools and resources for healthcare data science. The chapter includes references to the established tools and platforms for those who want to pursue data science in healthcare in more detail and become experts in the field.

Chapter five presents a case study of hospital readmission prediction for diabetes patients and provides a step-by-step R code for exploratory data analysis and machine learning models development. The link to the R script is also provided so that the reader can build the project in R themselves.

Chapters six and seven provide an overview of the applications of artificial intelligence and data science in clinical decision making and healthcare operations.

Chapter eight provides a brief overview of the ethical considerations of data science in healthcare.

In the end, Chapter nine gives future directions and challenges in healthcare data science as something to consider for further studies and explorations.

In summary, the book provides:

step-by-step approach to designing and developing data science projects in healthcare

easy to understand structure to facilitate the development of data science projects for beginners

links to useful resources and tools (mostly free and open source) that help build and execute AI projects in healthcare

links to free-to-use healthcare databases

data science case study examples that demonstrate how to build data science projects

Target Audience of This Book

Students pursuing degrees or certifications in healthcare data science, health informatics, or medical sciences, as well as researchers in healthcare-related fields, can use this book as a learning resource.

This book targets graduate students, doctoral students and early career researchers who want to start their data science career but have no clue of how to design and develop data science projects in healthcare.

This book also targets those graduate students and early career researchers who have started their careers as data scientists and are having difficulty maintaining the momentum in the project development and execution process. The book breaks down the data science process into smaller concrete steps. The book