An Introduction to Healthcare Informatics: Building Data-Driven Tools

$19.99

Download An Introduction to Healthcare Informatics: Building Data-Driven Tools written by Peter McCaffrey in PDF format. This book is under the category Health and bearing the isbn/isbn13 number 0128149159; 0128149167/9780128149157/ 9780128149164. You may reffer the table below for additional details of the book.

SKU: 404dcc91b2ae Category: Tags: ,

Specifications

book-author

Peter McCaffrey

publisher

Academic Press

file-type

PDF

pages

542 pages

language

English

asin

B08F3CWF7J

isbn10

0128149159; 0128149167

isbn13

9780128149157/ 9780128149164


Book Description

An Introduction to Healthcare Informatics: Building Data-Driven Tools (PDF) bridges the gap between the current healthcare Information technology (IT) landscape and cutting edge technologies in cloud infrastructure; data science; artificial intelligence and application development. IT 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 use data and related tools.

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

  • Brings diagrams for each example and technology describing how they operate individually as well as how they fit into a larger reference architecture built upon throughout the textbook
  • Presents case-based learning relevant to healthcare; bringing each concept accompanied by an example which becomes critical when explaining the function of SQL; databases; basic models etc.
  • Explains healthcare-specific stakeholders and the management of analytical projects within healthcare; allowing healthcare practitioners to successfully navigate the political and bureaucratic challenges to implementation
  • Provides a roadmap for implementing modern technologies and design patterns in a healthcare setting; helping the reader to understand both the archaic enterprise systems that often exist in hospitals as well as emerging tools and how they can be used together.

NOTE: This sale only includes An Introduction to Healthcare Informatics: Building Data-Driven Tools in PDF. No access codes included.

Additional information

book-author

Peter McCaffrey

publisher

Academic Press

file-type

PDF

pages

542 pages

language

English

asin

B08F3CWF7J

isbn10

0128149159; 0128149167

isbn13

9780128149157/ 9780128149164

Table of contents


Table of contents :
Cover
An Introduction to Healthcare
Informatics:
Building Data-Driven Tools
Copyright
Dedication
Author’s biography
Foreword
Section 1: Storing and accessing data
The healthcare IT landscape
How we got here and the growth of healthcare IT
The role of informatics
Common architectural aspects of healthcare IT
Device and application levels
Communication level
Process level
Common organizational aspects of healthcare IT
Physician and nurse informaticists
Regulatory aspects of healthcare IT
Challenges and opportunities
Conclusion
Relational databases
A brief history of SQL and relational databases
Overview of the relational model
Differences between the relational model and SQL
Primary and foreign keys
ACID and transactions with data
Normalization
Conclusion
References
SQL
Getting started with SQL
Structure of SQL databases
Basic SQL: SELECT, FROM, WHERE, and ORDER BY statements
Basic SQL: GROUP BY and general aggregate functions
Intermediate SQL: Joins
Advanced SQL: Window functions
SQL concept: Indexes
SQL concept: Schemas
Advanced SQL: SubQueries
Conclusion
Example project 1: Querying data with SQL
Introduction and project background
Viewing tables
Querying tables
Average number of visits per day per location
Average patient age and patient sex per location
Average number of patient visits per provider
Counts of diagnosis codes and average age per clinic location
Conclusion
Nonrelational databases
Early nonrelational models
The rise of modern nonrelational models
Key-value stores
Document stores
Column stores
Traditional column stores
Wide column stores
Graph databases
Conclusion
Reference
M/MUMPS
A brief history and context
The M language
General concepts regarding arrays and MUMPS
Arrays and MUMPS
MUMPS, globals, and data infrastructure
Conclusion
References
Section 2: Understanding Healthcare Data
How to approach healthcare data questions
Introduction
Healthcare as a CAS
Drivers of fallacy: Chance and bias
Missingness
Selecting tractable areas for intervention
Data and trust
Conclusion
Clinical and administrative workflows: Encounters, laboratory testing, clinical notes, and billing
Introduction
Encounters, patients, and episodes of care
Laboratory testing, imaging, and medication administration
Clinical notes and documentation
Billing
Conclusion
HL-7, clinical documentation architecture, and FHIR
Introduction
HL7 and HL7v2
RIM, HL7v3, and clinical documentation architecture
FHIR
DICOM
Vendor standards
Cloud services
Conclusion
References
Ontologies, terminology mappings, and code sets
Introduction
Diagnostic ontologies: ICD and ICD-CM
Procedure ontologies: ICD-PCS, CPT, and HCPCS
General ontologies: SNOMED, SNOMED-CT
Other specific ontologies: LOINC and NDC
Summative ontologies: DRG
Conclusion
References
Section 3: Analyzing Data
A selective introduction to Python and key concepts
Python: What and why
A note on Python 2 and 3
General structure of the language
Type system
Control flow
Functions
Objects
Basic data structures
Lists
Sets
Tuples
Dictionaries
List and dictionary comprehensions
Conclusion
Reference
Packages, interactive computing, and analytical documents
Introduction
Packages and package management
Key packages
Jupyter
Analytical documents and interactive computing
Conclusion
Assessing data quality, attributes, and structure
Introduction
Importing, cleaning, and assessing data
Tidying data
Handling missing values
Conclusion
Introduction to machine learning: Regression, classification, and important concepts
The aim of machine learning
Regression
Functions as hypotheses
Error and cost
Optimization
Classification
Additional considerations: Normalization, regularization, and generalizability
Conclusion
Introduction to machine learning: Support vector machines, tree-based models, clustering, and explainability
Introduction
Support vector machines
Decision trees
Clustering
Model explainability
Conclusion
Computational phenotyping and clinical natural language processing
Introduction
Manual review and general review considerations
Computational phenotyping: General considerations
Supervised methods
Unsupervised methods
Natural language processing
Conclusion
Example project 2: Assessing and modeling data
Introduction and project background
Data collection
Data assessment and preparation
Model creation
Logistic regression
Decision tree
Support vector machine
Exporting and persisting models
Conclusion
Introduction to deep learning and artificial intelligence
Introduction
What exactly is deep learning
Feed forward networks
Training and backpropagation
Local versus global minima
Convolutional networks
Adversarial examples and local minima
Recurrent networks
Autoencoders and generative adversarial networks
Conclusion
Reference
Section 4: Designing Data Applications
Analysis best practices
Introduction
Workflow
Documentation
Data governance
Conclusion
Overview of big data tools: Hadoop, Spark, and Kafka
Introduction
Hadoop
Spark
Kafka
Conclusion
Cloud technologies
Introduction
Data storage
Compute
Machine learning and analysis services
Conclusion
Index
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
Back Cover

Recent Posts

5 tips for a good business blog

Follow my blog with BloglovinAre you also looking for a good structure for your business blogs? That you finally have a serious and good structure for all your texts that are online? On your website but also on social media. In this review you will find 5 tips from Susanna Florie from her…

Study tips from a budding engineer

“Why engineering?” is a question I get often. The answer for me is simple: I like to solve problems. Engineering is a popular field for many reasons. Perhaps this is because almost everything around us is created by engineers in one way or another, and there are always new, emerging and exciting technologies impacting…

How do I study mathematics and pass my exam?

Not sure how best to study math ? Are you perhaps someone who starts studying the day before the exam? Then you know yourself that your situation is not the most ideal. Unfortunately, there is no magic bullet to make you a maths crack or pass your exam in no time . It is important to know that mathematics always builds on…

(0)
×