Healthcare Analytics Made Simple: Techniques in healthcare computing using machine learning and Python - Couverture souple
Synopsis
"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"--
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Dr. Vikas (Vik) Kumar grew up in the United States in Niskayuna, New York. He earnedhis MD from the University of Pittsburgh, but shortly afterwards he discovered his truecalling of computers and data science. He then earned his MS in the College of Computingat Georgia Institute of Technology and has subsequently worked as a data scientist for bothhealthcare and non-healthcare companies. He currently lives in Atlanta, Georgia.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Healthcare Analytics Made Simple: Techniques in healthcare computing using machine learning and Python
Edité par
Packt Publishing, 2018
ISBN 10 : 1787286703
ISBN 13 : 9781787286702
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Healthcare Analytics Made Simple: Techniques in healthcare computing using machine learning and Python
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Healthcare Analytics Made Simple
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ISBN 13 : 9781787286702
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Healthcare Analytics Made Simple
Edité par
Packt Publishing, 2018
ISBN 10 : 1787286703
ISBN 13 : 9781787286702
Ancien ou d'occasion
Couverture souple
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Healthcare Analytics Made Simple: Techniques in healthcare computing using machine learning and Python
Edité par
Packt Publishing, 2018
ISBN 10 : 1787286703
ISBN 13 : 9781787286702
Neuf
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Healthcare Analytics Made Simple (Paperback)
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ISBN 10 : 1787286703
ISBN 13 : 9781787286702
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Paperback. Etat : new. Paperback. Add a touch of data analytics to your healthcare systems and get insightful outcomesKey FeaturesPerform healthcare analytics with Python and SQLBuild predictive models on real healthcare data with pandas and scikit-learnUse analytics to improve healthcare performanceBook DescriptionIn 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 learnGain valuable insight into healthcare incentives, finances, and legislation Discover the connection between machine learning and healthcare processesUse SQL and Python to analyze dataMeasure healthcare quality and provider performanceIdentify features and attributes to build successful healthcare models Build predictive models using real-world healthcare dataBecome an expert in predictive modeling with structured clinical dataSee what lies ahead for healthcare analyticsWho this book is forHealthcare 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. Machine learning and analytics have been widely utilized across the healthcare sector of late. This book will bridge the gap between practicing doctors and you as a data scientist. You will learn how to work with healthcare data and gain better insight from this data to improve healthcare outcomes. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9781787286702
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Healthcare Analytics Made Simple
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Healthcare Analytics Made Simple
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Packt Publishing Limited, GB, 2018
ISBN 10 : 1787286703
ISBN 13 : 9781787286702
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Paperback. Etat : New. Add a touch of data analytics to your healthcare systems and get insightful outcomesKey FeaturesPerform healthcare analytics with Python and SQLBuild predictive models on real healthcare data with pandas and scikit-learnUse analytics to improve healthcare performanceBook DescriptionIn 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 learnGain valuable insight into healthcare incentives, finances, and legislationDiscover the connection between machine learning and healthcare processesUse SQL and Python to analyze dataMeasure healthcare quality and provider performanceIdentify features and attributes to build successful healthcare modelsBuild predictive models using real-world healthcare dataBecome an expert in predictive modeling with structured clinical dataSee what lies ahead for healthcare analyticsWho this book is forHealthcare 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. N° de réf. du vendeur LU-9781787286702
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Healthcare Analytics Made Simple
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ISBN 10 : 1787286703
ISBN 13 : 9781787286702
Neuf
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