Knowledge Discovery in Nursing Minimum Data Set Using Data Mining

다국어 초록 (Multilingual Abstract)

Purpose. The purposes of this study were to apply data mining tool to nursing specific knowledge discovery
process and to identify the utilization of data mining skill for clinical decision making.
Methods. Data mining based on rough set model was conducted on a large clinical data set containing NMDS elements.
Randomized 1000 patient data were selected from year 1998 database which had at least one of the
five most frequently used nursing diagnoses. Patient characteristics and care service characteristics including
nursing diagnoses, interventions and outcomes were analyzed to derive the meaningful decision rules.
Results. Number of comorbidity, marital status, nursing diagnosis related to risk for infection and nursing intervention
related to infection protection, and discharge status were the predictors that could determine the
length of stay. Four variables (age, impaired skin integrity, pain, and discharge status) were identified as
valuable predictors for nursing outcome, relived pain. Five variables (age, pain, potential for infection, marital
status, and primary disease) were identified as important predictors for mortality.
Conclusions. This study demonstrated the utilization of data mining method through a large data set with standardized
language format to identify the contribution of nursing care to patient s health.

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