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

Yao Yi Kuo, Shu Tien Huang, Hung Wen Chiu

研究成果: 雜誌貢獻文章同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
文章編號290
期刊BMC Medical Informatics and Decision Making
21
發行號1
DOIs
出版狀態已發佈 - 12月 2021

ASJC Scopus subject areas

  • 健康政策
  • 健康資訊學

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