Anthony Wong, MTech 1 , Senthil K. Nachimuthu, MD 1 , Peter J. Haug, MD 1,2

1. Finding Patterns in Heart Failure Readmissions with Temporal Data Mining Anthony Wong, MTech1, Senthil K. Nachimuthu, MD1, Peter J. Haug, MD1,2 1 Department of Biomedical Informatics, University of Utah, 2Intermountain Healthcare, Salt Lake City, Utah • Introduction • The American Heart Association reported that 5.8 million people in the US have heart failure (HF), and among them, 23% are at risk of having readmission within 30 days. • Clinicians care about readmission because they are commonly associated with adverse outcomes in patients, quality of care and cost. • Clinicians often consider prior events and trends when making decisions at the point of care. • Temporal data mining concerns with analyzing large data set with temporal interdependencies to discover meaningful patterns. Radar Charts for Cluster Medoids • HF Readmission Model • Deidentified data from 148 patients (100 for training, 48 for testing). • Our model combines: • Temporal Variables - blood pressure (diastolic & systolic), heart rate, respiratory rate, and body temperature. • Non-Temporal Variables – age, length of stay, cardiovascular disease diagnosed (6 variables), discharge medication (8 variables). Breakdown of Cluster Medoids for Blood Pressure, Diastolic • We clustered each of the vital signs independently into 5 medoids (cluster centers). A medoid is also the object (a sequence) of the cluster. • Clusters were trained using150 samples. • Discussion • Patterns revealed rules to identify potential patients of being readmitted within 30 days. • Temporal clustering enabled us to create temporal models that can be solved using classical machine learning approach by combining temporal and non-temporal variables. • Results suggest that our temporal models have modest improvement over the readmission model reported by Krumholz et al, 2008 (ROC = 0.61). • Temporal modeling can potentially enhanced prediction of readmission. • Limitation • Variable length time series are difficult to analyze. • Issues with data accuracy and completeness. • Objectives • To find predictive patterns for HF readmission within 30 days using temporal data mining techniques. • To analyze and evaluate the performance of HF readmission model. • Patterns and Rules • Vital signs medoids were evaluated using decision table classification algorithm. • Feature selected diastolic blood pressure and heart rate. • One candidate rule to predict • readmission within 30 days • Rules accuracy is 68%. • Area under the ROC is 0.641. • Methodology • Preliminary Study: We analyzed retrospectively a subset of inpatient records containing patients with history of HF to develop a baseline predictive algorithm for readmission prediction. • Deidentified clinical data contains: • Patients first identified with HF or ejection fraction less than 40% in 2009. • Patient’s age ≥ 65 years old. • Has subsequent admission after discharge. • Temporal Data Mining • We selected patient’s vital signs as temporal variables. Sequences were clustered to identify general patterns that could represent trends. • K-Medoidsclustering algorithm to minimize the sum of dissimilarities between sequences to create clusters. • Dynamic Time Warping (DTW) to compute distances between two time series. Useful to measure similarity between two sequences of varying length. Conclusion We demonstrated that temporal data mining is a viable method to discover clinically useful patterns to detect risk for HF readmission among patients. AND • Results • We interpreted the rule predicting HF readmission within 30 days as: • Patient admitted with low diastolic BP and continue to remain low during long hospitalization. • Heart rate remains high. • Area under the ROC for HF Readmission Model prediction using Logistic Regression is 0.638. ROC for HF Readmission Model Data provided by Intermountain Healthcare. Temporal models analyzed with Machine Learning Python (MLPY) and Weka. Anthony Wonganthony.wong@utah.edu