Mining association rules from a pediatric primary care decision support system.

The purpose of this study was to apply an unsupervised data mining algorithm to a database containing data collected at the point of care for clinical decision support. The data set was taken from the Child Health Improvement Program (CHIP), a preventive services tracking and reminder system in use at the University of North Carolina. The database contains over 30,000 visits. We used a previously described pattern discovery algorithm to extract 2nd and 3rd order association rules from the data and reviewed the literature two see if the associations had been described before. The algorithm discovered 16 2nd order associations and 103 3rd order associations. The 3rd order associations contained no new information. The 2nd order associations demonstrated a covariance among a range of health risk behaviors. Additionally, the algorithm discovered that both tobacco smoke exposure and chronic cardiopulmonary disease are associated with failure on developmental screens. These relationships have been described before and have been attributed to underlying poverty. The work demonstrates the ability of unsupervised data mining by rule association on sparse clinical data to discover clinically important associations. However, many associations may be previously known or explained by confounding variables.