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

1. Predicting Sepsis in the ICU using Dynamic Bayesian Networks 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 • 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. Sepsis Temporal Model • 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 • 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. • 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. • 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 • 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. Conclusion We have demonstrated that it is possible to develop a temporal model using a DBN by structuring the model using clinical knowledge. Descriptive Statistics Data provided by Intermountain Healthcare. Anthony Wonganthony.wong@utah.edu