A Convolutional Neural Network Model for Prediction of ICU Performance Metrics: Time Series and Image Transformation Approaches

Ömer Karahan, Yasin Ulukuş, Çiğdem Erdem

2023

Abstract

In our study we used Convolutional Neural Network (CNN) to predict Intensive Care Unit (ICU) performances of patients via images generated from patients’ Sequential Organ Failure Assessment (SOFA) scores which are used to assess the acute morbidity of intensive care unit patients. In our study we propose a novel method to predict ICU performances; mortality during the stay in ICU, mortality in one year after discharge from ICU, readmission and length of stay of ICU patients. We trained CNN models with images generated from multivariate time series data. Our model development process consists of two steps; converting SOFA scores of patients into an image and training the CNN with generated images to predict patients’ ICU performances. We search for the best performing image generation algorithm which has the highest AUROC value for each prediction. Our model gives us AUROC values for mortality in ICU, readmission after discharge from ICU and length of stay of patients in ICU as 0.83, 0.84, 0.87, 0.56 respectively. We compare our methods’ performance with random forest, support vector machine, Logistic regression and ensemble of these algorithms. The proposed image-based method in which we use the first day SOFA scores outperform the random forest, support vector machine and logistic regression algorithms. Our method performed similar to the studies in literature in terms of predicting mortality in ICU using first day data with an AUROC value of 0.83. Our model’s performance would be improved with further feature engineering.

Paper Citation

in Harvard Style

Karahan Ö., Ulukuş Y. and Erdem Ç. (2023). A Convolutional Neural Network Model for Prediction of ICU Performance Metrics: Time Series and Image Transformation Approaches. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: CCH, ISBN 978-989-758-631-6, pages 671-679. DOI: 10.5220/0011925500003414

in Bibtex Style

@conference{cch23,
author={Ömer Karahan and Yasin Ulukuş and Çiğdem Erdem},
title={A Convolutional Neural Network Model for Prediction of ICU Performance Metrics: Time Series and Image Transformation Approaches},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: CCH,},
year={2023},
pages={671-679},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011925500003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: CCH,
TI - A Convolutional Neural Network Model for Prediction of ICU Performance Metrics: Time Series and Image Transformation Approaches
SN - 978-989-758-631-6
AU - Karahan Ö.
AU - Ulukuş Y.
AU - Erdem Ç.
PY - 2023
SP - 671
EP - 679
DO - 10.5220/0011925500003414