Finding temporal patterns — A set-based approach
We created an inference engine and query language for expressing temporal patterns in data. The patterns are represented by using temporally-ordered sets of data objects. Patterns are elaborated by reference to new objects inferred from original data, and by interlocking temporal and other relations...
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| Veröffentlicht in: | Artificial intelligence in medicine 1994-06, Vol.6 (3), p.263-271 |
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| container_end_page | 271 |
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| container_issue | 3 |
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| container_title | Artificial intelligence in medicine |
| container_volume | 6 |
| creator | Wade, Ted D. Byrns, Patricia J. Steiner, John F. Bondy, Jessica |
| description | We created an inference engine and query language for expressing temporal patterns in data. The patterns are represented by using temporally-ordered sets of data objects. Patterns are elaborated by reference to new objects inferred from original data, and by interlocking temporal and other relationships among sets of these objects. We found the tools well-suited to define scenarios of events that are evidence of inappropriate use of prescription drugs, using Medicaid administrative data that describe medical events. The tools' usefulness in research might be considerably more general. |
| doi_str_mv | 10.1016/0933-3657(94)90066-3 |
| format | Article |
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| fulltext | fulltext |
| identifier | ISSN: 0933-3657 |
| ispartof | Artificial intelligence in medicine, 1994-06, Vol.6 (3), p.263-271 |
| issn | 0933-3657 1873-2860 |
| language | eng |
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| source | MEDLINE; Elsevier ScienceDirect Journals |
| subjects | Artificial Intelligence Computer applications Database Management Systems Drug Prescriptions Drug treatment Expert Systems Humans Inference engine Information Systems Knowledge representation Medicaid Medicine Neural Networks (Computer) Pattern Recognition, Automated Programming Languages Prolog Records as Topic Set Software Design Temporal pattern Temporal patterns Time Factors |
| title | Finding temporal patterns — A set-based approach |
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