Temporal Data Mining in Electronic Medical Records from Patients with Acute Coronary Syndrome
MetadataShow full item record
Every 25 seconds someone in the US has a cardiac event and one person per minute will die from it. ST-elevated myocardial infarction (STEMI), non ST-elevated myocardial infarction and unstable angina are caused by ischemia and referred to as acute coronary syndrome (ACS). STEMI is the most severe and accounts for a quarter of ACS cases. Research in STEMI treatment tends to focus on a single event and the risks/benefits thereof. The interaction between events during an encounter is especially important in STEMI, where the timing of treatments is crucial for positive patient outcomes. However there is a dearth of research into the temporal relationships between events. To explore temporal relationships between events, I created a sequential pattern mining algorithm (SPM) and a temporal association rule mining algorithm (TARM) to mine the Acute Coronary Syndrome Patient Database (ACSPD). The ACSPD is a large, 9 year EMR database derived from 128 healthcare institutions across the US. I simulated data to examine SPM performance and found that it is well-suited to extract patterns from noisy data. The TARM is designed to discover rules comprised of 3 temporally ordered events, i.e. clinical practice patterns (CPP). Using the SPM in the ACSPD, I discovered 39 order sets. Not all order sets are present for the 9 year span and overall order set use drops in 2004. I postulate that this denotes a shift in medical practice. In late 2004, the American Heart Association (AHA) published new STEMI treatment guidelines. I condensed the ACSPD sequences using the order sets then applied TARM. Using support, confidence, Bayes' factor, lift, likelihood, and Zhang's measures, I found substantial variation, rarity and weak antecedent-consequent pairing in the CPPs. To explore the interaction between clinical decisions and patient outcomes, I compared the CPPs with AHA STEMI performance measures for compliance and analyzed the risk of bleeding and mortality. CPP compliance with STEMI performance measures decreases mortality and bleeding risk, but there is evidence of complex interactions between measures that augments/masks the effect. The contributions of this work are 1) exploring CPPs and their effect on patient outcomes using EMR big data and 2) algorithm performance evaluation using simulation.