Name
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. |
Year | Venue | Keywords |
|---|---|---|
1999 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION | programming languages,database management systems |
Field | DocType | Issue |
Data warehouse,Health care,Row,Data science,SQL,Computer science,Health services research,Patient care,Time complexity,Schema (psychology) | Conference | SUPnan |
ISSN | Citations | PageRank |
1067-5027 | 16 | 1.75 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stephen B. Johnson | 1 | 16 | 1.75 |
| Damianos Chatziantoniou | 2 | 16 | 2.76 |