Healthcare Analytics Made Simple

Healthcare Analytics Made Simple
Author: Vikas (Vik) Kumar
Publisher: Packt Publishing Ltd
Total Pages: 258
Release: 2018-07-31
Genre: Computers
ISBN: 1787283224

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Add a touch of data analytics to your healthcare systems and get insightful outcomes 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 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 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.

Big Data Analytics Made Easy

Big Data Analytics Made Easy
Author: Y. Lakshmi Prasad
Publisher: Notion Press
Total Pages: 316
Release: 2016-12-14
Genre: Computers
ISBN: 1946390720

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Big Data Analytics Made Easy is a must-read for everybody as it explains the power of Analytics in a simple and logical way along with an end to end code in R. Even if you are a novice in Big Data Analytics, you will still be able to understand the concepts explained in this book. If you are already working in Analytics and dealing with Big Data, you will still find this book useful, as it covers exhaustive Data Mining Techniques, which are considered to be Advanced topics. It covers Machine Learning concepts and provides in-depth knowledge on unsupervised as well as supervised Learning, which is very important for decision-making. The toughest Data Analytics concepts are made simpler, It features examples from all the domains so that the reader gets connected to the book easily. This book is like a personal trainer that will help you master the Art of Data Science.

Healthcare Analytics for Quality and Performance Improvement

Healthcare Analytics for Quality and Performance Improvement
Author: Trevor L. Strome
Publisher: John Wiley & Sons
Total Pages: 246
Release: 2013-10-02
Genre: Business & Economics
ISBN: 1118760158

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Improve patient outcomes, lower costs, reduce fraud—all with healthcare analytics Healthcare Analytics for Quality and Performance Improvement walks your healthcare organization from relying on generic reports and dashboards to developing powerful analytic applications that drive effective decision-making throughout your organization. Renowned healthcare analytics leader Trevor Strome reveals in this groundbreaking volume the true potential of analytics to harness the vast amounts of data being generated in order to improve the decision-making ability of healthcare managers and improvement teams. Examines how technology has impacted healthcare delivery Discusses the challenge facing healthcare organizations: to leverage advances in both clinical and information technology to improve quality and performance while containing costs Explores the tools and techniques to analyze and extract value from healthcare data Demonstrates how the clinical, business, and technology components of healthcare organizations (HCOs) must work together to leverage analytics Other industries are already taking advantage of big data. Healthcare Analytics for Quality and Performance Improvement helps the healthcare industry make the most of the precious data already at its fingertips for long-overdue quality and performance improvement.

Machine Learning for Healthcare Analytics Projects

Machine Learning for Healthcare Analytics Projects
Author: Eduonix Learning Solutions
Publisher: Packt Publishing Ltd
Total Pages: 131
Release: 2018-10-30
Genre: Computers
ISBN: 1789532523

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Create real-world machine learning solutions using NumPy, pandas, matplotlib, and scikit-learn Key FeaturesDevelop a range of healthcare analytics projects using real-world datasetsImplement key machine learning algorithms using a range of libraries from the Python ecosystemAccomplish intermediate-to-complex tasks by building smart AI applications using neural network methodologiesBook Description Machine Learning (ML) has changed the way organizations and individuals use data to improve the efficiency of a system. ML algorithms allow strategists to deal with a variety of structured, unstructured, and semi-structured data. Machine Learning for Healthcare Analytics Projects is packed with new approaches and methodologies for creating powerful solutions for healthcare analytics. This book will teach you how to implement key machine learning algorithms and walk you through their use cases by employing a range of libraries from the Python ecosystem. You will build five end-to-end projects to evaluate the efficiency of Artificial Intelligence (AI) applications for carrying out simple-to-complex healthcare analytics tasks. With each project, you will gain new insights, which will then help you handle healthcare data efficiently. As you make your way through the book, you will use ML to detect cancer in a set of patients using support vector machines (SVMs) and k-Nearest neighbors (KNN) models. In the final chapters, you will create a deep neural network in Keras to predict the onset of diabetes in a huge dataset of patients. You will also learn how to predict heart diseases using neural networks. By the end of this book, you will have learned how to address long-standing challenges, provide specialized solutions for how to deal with them, and carry out a range of cognitive tasks in the healthcare domain. What you will learnExplore super imaging and natural language processing (NLP) to classify DNA sequencingDetect cancer based on the cell information provided to the SVMApply supervised learning techniques to diagnose autism spectrum disorder (ASD)Implement a deep learning grid and deep neural networks for detecting diabetesAnalyze data from blood pressure, heart rate, and cholesterol level tests using neural networksUse ML algorithms to detect autistic disordersWho this book is for Machine Learning for Healthcare Analytics Projects is for data scientists, machine learning engineers, and healthcare professionals who want to implement machine learning algorithms to build smart AI applications. Basic knowledge of Python or any programming language is expected to get the most from this book.

Healthcare Data Analytics

Healthcare Data Analytics
Author: Chandan K. Reddy
Publisher: CRC Press
Total Pages: 756
Release: 2015-06-23
Genre: Business & Economics
ISBN: 148223212X

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At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear understanding of the analytical techniques currently available

Statistics Made Simple for School Leaders

Statistics Made Simple for School Leaders
Author: Susan Rovezzi Carroll
Publisher: R&L Education
Total Pages: 162
Release: 2002-10-16
Genre: Education
ISBN: 146165419X

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The chief executive officer of a corporation is not much different from a public school administrator. While CEOs base many of their decisions on data, for school administrators, this type of research may conjure up miserable memories of searching for information to meet a graduate school requirement. However, the value of data-based decision making will continue to escalate and the school community—students, teachers, parents and the general public—expect this information to come from their administrators. Administrators are called on to be accountable, but few are capable of presenting the mountain of data that they collect in a cohesive and strategic manner. Most statistical books are focused on statistical theory versus application, but Statistics Made Simple for School Leaders presents statistics in a simple, practical, conceptual, and immediately applicable manner. It enables administrators to take their data and manage it into strategic information so the results can be used for action plans that benefit the school system. The approach is 'user friendly' and leaves the reader with a confident can-do attitude to communicate results and plans to staff and the community.

Healthcare Made Easy

Healthcare Made Easy
Author: Michelle Katz
Publisher: Adams Media
Total Pages: 0
Release: 2014-12-05
Genre: Business & Economics
ISBN: 9781440580192

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An easy-to-understand guide to the Affordable Care Act! Written by ABC World News' Real Money expert and healthcare advocate Michelle Katz, Healthcare Made Easy is the ultimate resource for understanding and navigating the Affordable Care Act. Using simple and easy-to-follow language, she answers important questions about the ACA, such as: Are there any exemptions to buying insurance under the Affordable Care Act? What is the new "80/20 Rule"? And what exactly is the Early Retiree Reinsurance Program? What does the ACA mean for small businesses? What benefits must you offer and what are the tax breaks? What is covered for your children under the new CHIP laws? How can you get pharmaceutical companies to lower your prescription costs? With the expert advice in Healthcare Made Easy, you will learn how to get the most out of your healthcare plan without having to sacrifice your needs or empty your pockets. "Here is a guide, a map through the maze of healthcare decisions facing us all. Michelle has helped a lot of families. She is a champion of the consumer. A truly informed advocate." --Diane Sawyer "Michelle breaks down complicated laws and insurance regulations to make them understandable to everyone. She provides you with a road map to getting the best healthcare at the most affordable prices." --Tavis Smiley, Author, Television and Radio Host

Data Analytics in Biomedical Engineering and Healthcare

Data Analytics in Biomedical Engineering and Healthcare
Author: Kun Chang Lee
Publisher: Academic Press
Total Pages: 298
Release: 2020-10-18
Genre: Science
ISBN: 0128193158

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Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks. Examines the development and application of data analytics applications in biomedical data Presents innovative classification and regression models for predicting various diseases Discusses genome structure prediction using predictive modeling Shows readers how to develop clinical decision support systems Shows researchers and specialists how to use hybrid learning for better medical diagnosis, including case studies of healthcare applications using the MapReduce and Hadoop frameworks

Data Visualization Made Simple

Data Visualization Made Simple
Author: Kristen Sosulski
Publisher: Routledge
Total Pages: 272
Release: 2018-09-27
Genre: Business & Economics
ISBN: 135138077X

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Data Visualization Made Simple is a practical guide to the fundamentals, strategies, and real-world cases for data visualization, an essential skill required in today’s information-rich world. With foundations rooted in statistics, psychology, and computer science, data visualization offers practitioners in almost every field a coherent way to share findings from original research, big data, learning analytics, and more. In nine appealing chapters, the book: examines the role of data graphics in decision-making, sharing information, sparking discussions, and inspiring future research; scrutinizes data graphics, deliberates on the messages they convey, and looks at options for design visualization; and includes cases and interviews to provide a contemporary view of how data graphics are used by professionals across industries Both novices and seasoned designers in education, business, and other areas can use this book’s effective, linear process to develop data visualization literacy and promote exploratory, inquiry-based approaches to visualization problems.

Applied Health Analytics and Informatics Using SAS

Applied Health Analytics and Informatics Using SAS
Author: Joseph M. Woodside
Publisher:
Total Pages: 378
Release: 2018-11
Genre: Computers
ISBN: 9781642953404

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Leverage health data into insight! Applied Health Analytics and Informatics Using SAS describes health anamatics, a result of the intersection of data analytics and health informatics. Healthcare systems generate nearly a third of the world's data, and analytics can help to eliminate medical errors, reduce readmissions, provide evidence-based care, demonstrate quality outcomes, and add cost-efficient care. This comprehensive textbook includes data analytics and health informatics concepts, along with applied experiential learning exercises and case studies using SAS Enterprise MinerTM within the healthcare industry setting. Topics covered include: Sampling and modeling health data - both structured and unstructured Exploring health data quality Developing health administration and health data assessment procedures Identifying future health trends Analyzing high-performance health data mining models Applied Health Analytics and Informatics Using SAS is intended for professionals, lifelong learners, senior-level undergraduates, graduate-level students in professional development courses, health informatics courses, health analytics courses, and specialized industry track courses. This textbook is accessible to a wide variety of backgrounds and specialty areas, including administrators, clinicians, and executives.