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Background: Sepsis is the leading cause of in-hospital deaths, and it is one of the costliest complications to treat. Detection of sepsis is complicated and not yet efficient. Each hour of delay in treatment for a septic patient results in a 4-8% increase in chance of mortality.Method: The dataset provided consists of files that contain hourly parameter measurements for over 40,000 unique patients. Due to the complex nature of this challenge problem, a model of similar complexity was necessary. A boosted random forest ensemble was chosen and developed in MATLAB in hopes of producing the best results for this challenge. The provided data was time padded for 8 additional hours&#x2019; worth of data, 10-fold cross-validated, and imputed with previous data. Many ensemble methods were tested with Random Under-Sampling Boosting performing the best. For this model, the hyper-parameters were optimized via a grid search to find an optimal model.Results: Using the optimized hyper-parameters along with the correct pre-processing techniques, a 10-fold average utility score of 0.421 was achieved on the training sets A and B combined. We participated in Physionet Challenge under the name SOS: Searching of Sepsis and the utility score on full test set is 0.314. Our official rank is #14.

Time-Padded Random Forest Ensemble to Capture Changes in Physiology Leading to Sepsis Development