Conference proceeding
Time-Padded Random Forest Ensemble to Capture Changes in Physiology Leading to Sepsis Development
2019 Computing in Cardiology (CinC), v 2019-, pp 1-4
Sep 2019
Abstract
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' 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.
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4 citations in Scopus
Details
- Title
- Time-Padded Random Forest Ensemble to Capture Changes in Physiology Leading to Sepsis Development
- Creators
- Ben Sweely - University of Tennessee at KnoxvilleAustin Park - University of Tennessee at KnoxvilleLia Winter - University of Tennessee at KnoxvilleLongjian Liu - Drexel UniversityXiaopeng Zhao - University of Tennessee at Knoxville
- Publication Details
- 2019 Computing in Cardiology (CinC), v 2019-, pp 1-4
- Publisher
- Creative Commons
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Scopus ID
- 2-s2.0-85081118938
- Other Identifier
- 991019173586704721