Data Mining in Uniform Hospital Discharge Data Set Using Rough Set Model

Abstract

Purpose: The purpose of this study were to apply rough set model to nursing knowledge discovery process. Method: Data mining based on rough set model was conducted on a large clinical data set containing Nursing Minimum Data Set elements. Randomized patient data were selected from Uniform Hospital Discharge Data which had the frequently used nursing diagnoses. Patient and care characteristics including nursing diagnoses, interventions and outcomes were analyzed to derive the 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 to determine the length of stay. Age, impaired skin integrity, pain, and discharge status were identified as valuable predictors for nursing outcome, relived pain. Age, pain, potential for infection, marital status, and primary disease were identified as important predictors for mortality. Conclusion: This study demonstrated the utilization of Rough Set Model through a large data set with standardized language format to identify the contribution of specific care to patient’s health.