Dolores M. Peterson

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The goal of the research being reported is the discovery of useful concepts in temporal medical databases. Building on previous experiments, we introduce TEMPADIS, the Temporal Pattern Discovery System, which uses our Event Set Sequence approach to discover sequential patterns in medical data. We discuss problems unique to mining medical databases and(More)
The goal of the research being reported is the discovery of useful concepts in temporal medical databases. Building on previous experiments, we introduce TEMPADIS, the Temporal Pattern Discovery System, which uses our Event Set Sequence approach to discover patterns in this type of data. Results are presented for a database of Human Immunodeficiency Virus(More)
The goal of the research being reported is the discovery of useful concepts in temporal medical databases. In this paper, we present a sequence building approach, based on the Generalized Sequential Patterns (GSP) Algorithm (Srikant and Agrawal 1996), to discover temporal patterns in this type of data. We show that this pattern discovery is possible by(More)
Knowledge discovery in databases containing course-of-disease data for chronic illness is the thrust of the work detailed in this chapter. As seems to be typical of such databases, the data that is recorded is sparse and was collected with no concerted effort to maintain data quality. Despite this, we hypothesize that, given a database of clinical data for(More)
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