Introduction to Healthcare Informatics
portes grátis
Introduction to Healthcare Informatics
Building Data-Driven Tools
Mccaffrey, Peter
Elsevier Science Publishing Co Inc
07/2020
339
Mole
Inglês
9780128149157
15 a 20 dias
680
Descrição não disponível.
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
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Administration; Amazon web services; Anaconda; Analytical document; Antipattern; Artificial intelligence; Assessment; Azure; Big data; Classification; Cleanliness; Clinical documentation architecture; Cloud; CNN; Code; Complex adaptive systems; Computational phenotyping; CPT; Data cleaning; Data quality; Data storage; Data structures; Data warehouse; Decision tree; Deep learning; Design patterns; Document store; DRG; Explainability; Exploratory data analysis; FHIR; Google Cloud Platform; Hadoop; HCPCS; Healthcare billing; Healthcare data; Healthcare informatics; Healthcare IT; HL7; ICD; Implementation science; Informatics infrastructure; Information technology; Interactive computing; Interoperability; iPython; Jupyter; Kafka; Laboratory testing; Logistic regression; LOINC; Machine learning; Medical record processing; Messaging; Missingness; Modeling; MUMPS; Natural language processing; Nonrelational database; Notebook; Ontology; Operators; Organization; Patient selection; Perceptron; Process; Programming; Project; PySpark; Python; Query; Random forest; Regression; Regularization; Relational database; Relational Database; RNN; Schemaless; SNOMED; Software engineering; Spark; SQL; Standards; Terminology; Transparency; Workflow
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
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Administration; Amazon web services; Anaconda; Analytical document; Antipattern; Artificial intelligence; Assessment; Azure; Big data; Classification; Cleanliness; Clinical documentation architecture; Cloud; CNN; Code; Complex adaptive systems; Computational phenotyping; CPT; Data cleaning; Data quality; Data storage; Data structures; Data warehouse; Decision tree; Deep learning; Design patterns; Document store; DRG; Explainability; Exploratory data analysis; FHIR; Google Cloud Platform; Hadoop; HCPCS; Healthcare billing; Healthcare data; Healthcare informatics; Healthcare IT; HL7; ICD; Implementation science; Informatics infrastructure; Information technology; Interactive computing; Interoperability; iPython; Jupyter; Kafka; Laboratory testing; Logistic regression; LOINC; Machine learning; Medical record processing; Messaging; Missingness; Modeling; MUMPS; Natural language processing; Nonrelational database; Notebook; Ontology; Operators; Organization; Patient selection; Perceptron; Process; Programming; Project; PySpark; Python; Query; Random forest; Regression; Regularization; Relational database; Relational Database; RNN; Schemaless; SNOMED; Software engineering; Spark; SQL; Standards; Terminology; Transparency; Workflow
