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BMC Medical Informatics and Decision Making

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01-12-2021 | Septicemia | Research

Applying artificial neural network for early detection of sepsis with intentionally preserved highly missing real-world data for simulating clinical situation

Authors: Yao-Yi Kuo, Shu-Tien Huang, Hung-Wen Chiu

Published in: BMC Medical Informatics and Decision Making | Issue 1/2021

Abstract

Purpose

Some predictive systems using machine learning models have been developed to predict sepsis; however, they were mostly built with a low percent of missing values, which does not correspond with the actual clinical situation. In this study, we developed a machine learning model with a high rate of missing and erroneous data to enable prediction under missing, noisy, and erroneous inputs, as in the actual clinical situation.

Materials and methods

The proposed artificial neural network model was implemented using the MATLAB ANN toolbox, based on stochastic gradient descent. The dataset was collected over the past decade with approval from the appropriate institutional review boards, and the sepsis status was identified and labeled using Sepsis-3 clinical criteria. The imputation method was built by last observation carried forward and mean value, aimed to simulate clinical situation.

Results

The mean area under the receiver operating characteristic (ROC) curve (AUC) of classifying sepsis and nonsepsis patients was 0.82 and 0.786 at 0 h and 40 h prior to onset, respectively. The highest model performance was found for one-hourly data, demonstrating that our ANN model can perform adequately with limited hourly data provided.

Conclusions

Our model has the moderate ability to predict sepsis up to 40 h in advance under simulated clinical situation with real-world data.

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Title: Applying artificial neural network for early detection of sepsis with intentionally preserved highly missing real-world data for simulating clinical situation
Authors: Yao-Yi Kuo
Shu-Tien Huang
Hung-Wen Chiu
Publication date: 01-12-2021
Publisher: BioMed Central
Keywords: Septicemia
Septicemia
Published in: BMC Medical Informatics and Decision Making / Issue 1/2021
Electronic ISSN: 1472-6947
DOI: https://doi.org/10.1186/s12911-021-01653-0

At a glance: The STEP trials

Springer Medicine

Applying artificial neural network for early detection of sepsis with intentionally preserved highly missing real-world data for simulating clinical situation

Abstract

Purpose

Materials and methods

Results

Conclusions

At a glance: The STEP trials

Springer Medicine

Abstract

Purpose

Materials and methods

Results

Conclusions

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