Authors:
Ömer Karahan
1
;
Yasin Ulukuş
2
and
Çiğdem Erdem
1
Affiliations:
1
Department of Computer Engineering, Marmara University, Istanbul, Turkey
;
2
Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey
Keyword(s):
SOFA, ICU, CNN, Multivariate Timeseries, Readmission, Mortality in ICU, Mortality after Discharge from ICU, Length of Stay in ICU.
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.
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