designing systems that can adapt to changing knowledge /
First Statement of Responsibility
Prakash M. Nadkarni
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource (xix, 396 pages) :
Other Physical Details
illustrations
SERIES
Series Title
Health informatics
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references and index
CONTENTS NOTE
Text of Note
What Is Metadata? -- Data Types in the Medical Record -- Metadata and the Use of XML -- Representing Structured Clinical Data -- Building the User Interface for Structured Clinical Data Capture -- Medical Decision Support Systems: General Considerations -- Challenges in Electronic Decision Support Implementation -- Infrastructure for Complex Clinical Decision Support: Rule Engines -- Decision Support Infrastructure: Workflows and Service-Oriented Architectures -- Complex Decision Support in Practice -- Extending the Entity-Attribute-Value Model -- Descriptive Metadata: An Introduction to Terminologies -- Descriptive Metadata: Implementing Large-Scale Biomedical Ontologies -- Clinical Study Data Management Systems -- Data Retrieval for Heterogeneous Data Models -- Metadata for Data Warehousing -- Biomedical Metadata Standards
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SUMMARY OR ABSTRACT
Text of Note
To build good systems, one needs both good development skills as well as a thorough knowledge of the problem one is trying to solve. Knowledge of software history - what has worked and what hasn't - also helps in these types of detailed projects. Metadata-Driven Software Systems in Biomedicine lays down some of the foundations and provides a knowledge-base to assist this process. The technical portion of the book consists of database schemas and working code that provide non-trivial examples for the practitioner who is conversant with software development and wishes to employ the approaches described in the book.¡ Eight of the ten chapters include case studies, while the book also includes extensible designs in biomedical applications: electronic medical records, clinical study data management systems, laboratory research support systems, ontologies, and production-rule subsystems. This book is therefore ideal for individuals who have to interact with large biomedical database systems in an information-technology or informatician capacity, build interfaces to such systems or design new systems themselves