@inproceedings{7880029fc837446fa9418f44cbe662eb,
title = "Pediatric Sepsis Phenotyping Using Vital Sign Trajectories",
abstract = "Sepsis can be life-threatening, which highlights the need to understand the condition's diverse phenotypes to enhance treatment effectiveness. Sepsis phenotypes are derived from 12-hour vital sign trajectories of children (N=12,824) with multiple organ dysfunction syndrome from 13 U.S. hospitals. Survival analysis of the two subgroups produced by hierarchical clustering (HAC) on pairwise trajectory similarity matrix from dynamic time warping (DTW) showed a hazards ratio of 4.7 for 30-day mortality, which was better than the stratification of subgroups from group-based trajectory modeling. The higher mortality subgroup from HAC on DTW displayed higher blood pressure and pulse but lower temperature, in addition to acidosis. This comprehensive analysis of phenotypes can greatly aid in early risk evaluation, tailored treatment approaches, and improved outcomes for pediatric sepsis patients.",
keywords = "Machine Learning, Mortality Prediction, Pediatrics, Sepsis, Time-series Analysis",
author = "Ding, {Yanyi Jenny} and Zhidi Luo and Mindy Szeto and Yuan Luo and Sanchez-Pinto, {L. Nelson}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 ; Conference date: 05-12-2023 Through 08-12-2023",
year = "2023",
doi = "10.1109/BIBM58861.2023.10386019",
language = "English (US)",
series = "Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4297--4303",
editor = "Xingpeng Jiang and Haiying Wang and Reda Alhajj and Xiaohua Hu and Felix Engel and Mufti Mahmud and Nadia Pisanti and Xuefeng Cui and Hong Song",
booktitle = "Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023",
address = "United States",
}