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Extended SQL for manipulating clinical warehouse data.

Authors: Stephen B. Johnson; Damianos Chatziantoniou;

Extended SQL for manipulating clinical warehouse data.

Abstract

Health care institutions are beginning to collect large amounts of clinical data through patient care applications. Clinical data warehouses make these data available for complex analysis across patient records, benefiting administrative reporting, patient care and clinical research. Data gathered for patient care purposes are difficult to manipulate for analytic tasks; the schema presents conceptual difficulties for the analyst, and many queries perform poorly. An extension to SQL is presented that enables the analyst to designate groups of rows. These groups can then be manipulated and aggregated in various ways to solve a number of useful analytic problems. The extended SQL is concise and runs in linear time, while standard SQL requires multiple statements with polynomial performance. The extensions are extremely powerful for performing aggregations on large amounts of data, which is useful in clinical data mining applications.

Related Organizations
Keywords

Databases as Topic, Medical Records Systems, Computerized, Clinical Laboratory Techniques, Data Interpretation, Statistical, Database Management Systems, Humans, Information Storage and Retrieval, Programming Languages, Health Services Research

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    influence
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Average
Top 10%
Average
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