
An Introduction to Healthcare Informatics
Building Data-Driven Tools
Description
Key Features
- 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.
- Provides a roadmap for implementing modern technologies and design patters 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
- 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
- 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 book
Readership
Graduate students, physicians, nurses, and several members of biomedical field
Table of Contents
Section 1: Storing and Accessing Data
1. The Healthcare IT Landscape
2. Relational Databases
3. SQL4. Example Project 1: Querying Data with SQL
5. Non-Relational Databases
6. M/MUMPSSection 2: Understanding Healthcare Data
7. How to Approach Healthcare Data Questions
8. Clinical and Administrative Workflows: Encounters, Laboratory Testing, Clinical Notes, and Billing
9. HL-7 and FHIR, and Clinical Document Architecture
10. Ontologies, Terminology Mappings and Code SetsSection 3: Analyzing Data
11. A Selective Introduction to Python and Key Concepts
12. Packages, Interactive Computing, and Analytical Documents
13. Assessing Data Quality, Attributes, and Structure
14. Introduction to Machine Learning: Regression, Classification, and Important Concepts
15. Introduction to Machine Learning: Support Vector Machines, Tree-Based Models, Clustering, and Explainability
16. Computational Phenotyping, and Clinical Natural Language Processing
17. Example Project 2: Assessing and Modeling Data18. Introduction to Deep Learning and Artificial Intelligence
Section 4: Designing Data Applications
19. Analysis Best Practices
20. Overview of Big Data Tools: Hadoop, Spark and Kafka
21. Cloud Technologies
Product details
- No. of pages: 339
- Language: English
- Copyright: © Academic Press 2020
- Published: July 29, 2020
- Imprint: Academic Press
- Paperback ISBN: 9780128149157
- eBook ISBN: 9780128149164
About the Author
Peter Mccaffrey
Affiliations and Expertise
Ratings and Reviews
Latest reviews
(Total rating for all reviews)
Xiu E. Thu Apr 21 2022
Comprehensive and Applicable
The selection of topics is well thought out and is a good comprehensive survey of what goes into tool development in healthcare from project design to implementation.