An Introduction to Healthcare Informatics
Building Data-Driven Tools
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Product details:
- Publisher Elsevier Science
- Date of Publication 29 July 2020
- ISBN 9780128149157
- Binding Paperback
- No. of pages339 pages
- Size 234x190 mm
- Weight 680 g
- Language English 82
Categories
Long description:
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.
MoreTable of Contents:
Section 1: Storing and Accessing Data1. The Healthcare IT Landscape2. Relational Databases3. SQL
4. Example Project 1: Querying Data with SQL5. Non-Relational Databases6. M/MUMPS
Section 2: Understanding Healthcare Data7. How to Approach Healthcare Data Questions8. Clinical and Administrative Workflows: Encounters, Laboratory Testing, Clinical Notes, and Billing9. HL-7 and FHIR, and Clinical Document Architecture10. Ontologies, Terminology Mappings and Code Sets
Section 3: Analyzing Data11. A Selective Introduction to Python and Key Concepts12. Packages, Interactive Computing, and Analytical Documents13. Assessing Data Quality, Attributes, and Structure14. Introduction to Machine Learning: Regression, Classification, and Important Concepts15. Introduction to Machine Learning: Support Vector Machines, Tree-Based Models, Clustering, and Explainability16. Computational Phenotyping, and Clinical Natural Language Processing17. Example Project 2: Assessing and Modeling Data
18. Introduction to Deep Learning and Artificial Intelligence
Section 4: Designing Data Applications19. Analysis Best Practices20. Overview of Big Data Tools: Hadoop, Spark and Kafka21. Cloud Technologies
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