An Introduction to Healthcare Informatics: Building Data-Driven Tools 1st Edition Peter Mccaffrey 0128149167 9780128149164

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Product details:

• ISBN 10: 0128149167
• ISBN 13: 9780128149164
• Author: Peter Mccaffrey

An Introduction to Healthcare Informatics: Building Data-Driven Tools bridges the gap between the current healthcare IT landscape and cutting edge technologies in data science, cloud infrastructure, application development and even artificial intelligence. Information technology encompasses several rapidly evolving areas, however healthcare as a field suffers from a relatively archaic technology landscape and a lack of curriculum to effectively train its millions of practitioners in the skills they need to utilize data and related tools.

The book discusses topics such as data access, data analysis, big data current landscape and application architecture. Additionally, it encompasses a discussion on the future developments in the field. This book provides physicians, nurses and health scientists with the concepts and skills necessary to work with analysts and IT professionals and even perform analysis and application architecture themselves.

Table of contents:

Chapter 1: The healthcare IT landscape

Chapter 2: Relational databases

Chapter 3: SQL

Chapter 4: Example project 1: Querying data with SQL

Chapter 5: Nonrelational databases

Chapter 6: M/MUMPS

Chapter 7: How to approach healthcare data questions

Chapter 8: Clinical and administrative workflows: Encounters, laboratory testing, clinical notes, an

Chapter 9: HL-7, clinical documentation architecture, and FHIR

Chapter 10: Ontologies, terminology mappings, and code sets

Chapter 11: A selective introduction to Python and key concepts

Chapter 12: Packages, interactive computing, and analytical documents

Chapter 13: Assessing data quality, attributes, and structure

Chapter 14: Introduction to machine learning: Regression, classification, and important concepts

Chapter 15: Introduction to machine learning: Support vector machines, tree-based models, clustering

Chapter 16: Computational phenotyping and clinical natural language processing

Chapter 17: Example project 2: Assessing and modeling data

Chapter 18: Introduction to deep learning and artificial intelligence

Chapter 19: Analysis best practices

Chapter 20: Overview of big data tools: Hadoop, Spark, and Kafka

Chapter 21: Cloud technologies

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