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Early Detection of Sepsis using Ensemblers

Machine Learning 2020-10-21 v1 Machine Learning
Authors: Shailesh Nirgudkar , Tianyu Ding
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Abstract

This paper describes a methodology to detect sepsis ahead of time by analyzing hourly patient records. The Physionet 2019 challenge consists of medical records of over 40,000 patients. Using imputation and weak ensembler technique to analyze these medical records and 3-fold validation, a model is created and validated internally. The model achieved an accuracy of 93.45% and a utility score of 0.271. The utility score as defined by the organizers takes into account true positives, negatives and false alarms.

Keywords

sepsis detection ensembler imputation patient records physionet challenge early prediction

Cite

@article{arxiv.2010.09938,
  title  = {Early Detection of Sepsis using Ensemblers},
  author = {Shailesh Nirgudkar and Tianyu Ding},
  journal= {arXiv preprint arXiv:2010.09938},
  year   = {2020}
}

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