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An introduction to healthcare inform...

McCaffrey, Peter,

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An introduction to healthcare informatics = building data-driven tools /

Record Type:	Electronic resources : Monograph/item
Title/Author:	An introduction to healthcare informatics/ Peter Mccaffrey, John Monahan.
Reminder of title:	building data-driven tools /
Author:	McCaffrey, Peter,
Published:	Amsterdam :Academic Press, : 2020.,
Description:	1 online resource.
Notes:	Includes index.
[NT 15003449]:	Section 1: Storing and Accessing Data 1. The Healthcare IT Landscape 2. Relational Databases 3. SQL 4. Example Project 1: Querying Data with SQL 5. Non-Relational Databases 6. M/MUMPS Section 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 Sets Section 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 Data 18. 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.
Subject:	Medical informatics. -
Online resource:	https://www.sciencedirect.com/science/book/9780128149157
ISBN:	9780128149164 (electronic bk.)

An introduction to healthcare informatics = building data-driven tools /
McCaffrey, Peter,

An introduction to healthcare informaticsbuilding data-driven tools /[electronic resource] :Peter Mccaffrey, John Monahan. - Amsterdam :Academic Press,2020. - 1 online resource.

Includes index.

Section 1: Storing and Accessing Data 1. The Healthcare IT Landscape 2. Relational Databases 3. SQL 4. Example Project 1: Querying Data with SQL 5. Non-Relational Databases 6. M/MUMPS Section 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 Sets Section 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 Data 18. 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.

ISBN: 9780128149164 (electronic bk.)

Nat. Bib. No.: GBB9J7179bnbSubjects--Topical Terms:

661258
Medical informatics.
Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: R858

Dewey Class. No.: 610.285

An introduction to healthcare informatics = building data-driven tools /
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300 $a 1 online resource.
500 $a Includes index.
505 0 $a Section 1: Storing and Accessing Data 1. The Healthcare IT Landscape 2. Relational Databases 3. SQL 4. Example Project 1: Querying Data with SQL 5. Non-Relational Databases 6. M/MUMPS Section 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 Sets Section 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 Data 18. 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.
650 0 $a Medical informatics. $3 661258
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