Mining Bayesian Networks to Forecast Adverse Outcomes Related to Acute Coronary Syndrome

One fascinating aspect of tool building for datamining is the application of a generalized datamining tool to a specific domain. Often times, this process results in a cross disciplinary analysis of both the datamining technique and the application of the results to the domain itself. This process of cross-disciplinary analysis often leads not only to improvements of the tool, but more importantly, to a better understanding of the underlying domain model for the domain experts involved. This paper presents the results of applying a datamining tool for identifying a Bayesian Network to represent a dataset of triage information taken from patients arriving at the emergency room with symptoms of Acute Coronary Syndrome. Specifically, a domain expert generated Bayesian Network and a mined Bayesian Network, both trained using the triage dataset, are compared for their accuracy in forecasting 30-day adverse outcomes for the patients represented in the dataset. The comparison, done using ROC...

• Type: Conference
• Year: 2004
• Where: FLAIRS
• Authors: Andy Novobilski, Francis M. Fesmire, David Sonnemaker
• Keywords: Artificial Intelligence, Bayesian Network, Datamining, Datamining Tool, FLAIRS 2004