1. Acknowledgements Contact Information Anthony Wong, MTech 1, Senthil K. Nachimuthu, MD 1, Peter J. Haug, MD 1,2 Sepsis Temporal Model Methodology Dynamic Bayesian Networks We designed a Dynamic Bayesian Network (HMM), to represent the causal and temporal relationships found in sepsis. Kevin Murphy’s Bayesian Network Toolkit (BNT) and Projeny were the two main tools utilized in this project. Discretization with Clustering Technique Clustering methods were used to discretize continuous data in each observed field. 114,752 temporal observations were used for clustering. We then compared two different parameters in K- means:  Euclidean distance (L2 Norm)  Manhattan (L1 Norm) Inference Parameters of the conditional probability table (CPT) were obtained through Expectation – Maximization (EM) method. Multiple time slices were inferred using the Junction Tree Algorithm. Discussion  DBN with K-Means L2 Norm discretization performed slightly better. However, the area under the ROC curves were not significantly different.  Both sensitivity and specificity did not produce satisfactory result.  Discretization with K-Means L1 Norm seemed to yield better sensitivity at higher cut-off. Limitation Due to the intensity of computation required for this type of modeling, we could only perform the initial test on a small set of patient data. We also assumed that the diagnosis for sepsis is true for all time slices in each patient. Introduction  Early diagnosis of sepsis is key for early intervention and reducing mortality due to severe sepsis or septic shock.  Clinicians often consider prior events and temporal trends when they monitor their patients.  Temporal relationships play an important role in the decision making process but are often overlooked when modeling the problem.  Dynamic Bayesian Networks (DBN) can provide a generalized causal probabilistic framework that can explicitly model temporal relationships between clinical variables. Descriptive Statistics Data provided by Intermountain Healthcare. Anthony Wong anthony.wong@utah.edu Predicting Sepsis in the ICU using Dynamic Bayesian Networks 1 Department of Biomedical Informatics, University of Utah, 2 Intermountain Healthcare, Salt Lake City, Utah Conclusion We have demonstrated that it is possible to develop a temporal model using a DBN by structuring the model using clinical knowledge. Results Area under the ROC curve were calculated using Trapezoidal rule:  DBN with L1 Norm: 0.52  DBN with L2 Norm: 0.54 Confusion Matrix at 85% Cut-off (L1 Norm) Confusion Matrix at 85% Cut-off (L2 Norm) ROC Curves Objectives To design a temporal model using DBN for predicting sepsis. To analyze and evaluate the performance of sepsis temporal models with different discretization methods. Data We analyzed retrospective data obtained from Intermountain Healthcare’s ICUs. Data set: Clinical data from 2 ICUs January 2006 – February 2009 100 randomly chosen adult patients from a total of 3,336 (18 years old and above) 6,469 temporal observations MeanStandard DeviationRangeMinimumMaximumnn (%) Age (years)45.4619.72671885100100.00 Heart Rate (beats/min)86.9716.2711626142636398.35 Respiratory Rate (breaths/min)22.847.283285333451069.71 Systolic BP (mm/Hg)128.5820.9615570225478173.89 Diastolic BP (mm/Hg)62.1812.9912026146478173.89 Temperature (°C)36.890.826.634.040.6279443.18 APCO253.6527.92148.922.1171.02043.15 White Blood Count10.566.5850.72.853.53385.22 SepsisNo Sepsis Positive Test 27661 Negative Test 5230 SepsisNo Sepsis Positive Test 28986 Negative Test 395