Open AccessUtilizing Informative Missingness for Early Prediction of Sepsis
Author(s) -
Janmajay Singh,
Kentaro Oshiro,
Raghava Krishnan,
Masahiro Sato,
Tomoko Ohkuma,
Noriji Kato
Publication year - 2020
Publication title -
2019 computing in cardiology (cinc)
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.257
H-Index - 55
ISSN - 2325-887X
ISBN - 978-1-7281-6936-1
DOI - 10.22489/cinc.2019.280
Subject(s) - bioengineering , computing and processing , signal processing and analysis
Aims: Physicians have to routinely make crucial decisions about patients’ health in the ICU. Sepsis affects about 35% of ICU patients, killing approximately 25% of the afflicted. In this paper, we aim to predict the occurrence of sepsis early by studying the missingness of physiological variables and using it with the overall trends in data.Methods: We chose XGBoost as our base model and tried several variations by changing hyperparameters, window sizes and imputation methods. To further improve the model, we used masking vectors to represent the missingness of features in the dataset. Additional modifications include shifting the Sepsis Label to earlier time steps and tuning the classification probability threshold to further improve the model’s performance.Results: The XGBoost model with a sliding window of size 5, no imputation, utilizing informative missingness of all temporal variables and trained on labels shifted by 3 hours before t optimal , achieved a Utility Score of 0.337 on the full test set. We identified as "CTL-Team" in the challenge and were officially ranked 5 th on the basis of this score.