Comparing artificial intelligence strategies for early sepsis detection in the ICU: an experimental study

This repository contains the code used in the paper with the same title. To cite the article, please use the following guide:

@article{solis2023comparing,
  title={Comparing artificial intelligence strategies for early sepsis detection in the ICU: an experimental study},
  author={Sol{\'\i}s-Garc{\'\i}a, Javier and Vega-M{\'a}rquez, Bel{\'e}n and Nepomuceno, Juan A and Riquelme-Santos, Jos{\'e} C and Nepomuceno-Chamorro, Isabel A},
  journal={Applied Intelligence},
  pages={1--15},
  year={2023},
  publisher={Springer}
}

Table of Contents

1. Abstract
2. Authors
3. Prerequisites
4. How to download and generate the data
5. What does the repository?
6. How to define a parameter optimization and an experiment?
7. How to launch a parameter optimization and an experiment?
8. How works the code of the repository?
9. License

Abstract

Comparing artificial intelligence strategies for early sepsis detection in the ICU: an experimental study

Sepsis is a life-threatening condition whose early recognition is key to improving outcomes for patients in intensive care units (ICUs). Artificial intelligence can play a crucial role in mining and exploiting health data for sepsis prediction. However, progress in this field has been impeded by a lack of comparability across studies. Some studies do not provide code, and each study independently processes a dataset with large numbers of missing values. Here, we present a comparative analysis of early sepsis prediction in the ICU by using machine learning (ML) algorithms and provide open-source code to the community to support future work. We reviewed the literature and conducted two phases of experiments. In the first phase, we analyzed five imputation strategies for handling missing data in a clinical dataset (which is often sampled irregularly and requires hand-crafted preprocessing steps). We used the MIMIC-III dataset, which includes more than 5,800 ICU hospital admissions from 2001 to 2012. In the second phase, we conducted an extensive experimental study using five ML methods and five popular deep learning models. We evaluated the performance of the methods by using the area under the precision-recall curve, a standard metric for clinical contexts. The deep learning methods (TCN and LSTM) outperformed the other methods, particularly in early detection tasks more than 4 hours before sepsis onset. The motivation for this work was to provide a benchmark framework for future research, thus enabling advancements in this field.

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