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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number290
JournalBMC Medical Informatics and Decision Making
Volume21
Issue number1
DOIs
Publication statusPublished - Dec 2021

Keywords

  • Artificial intelligence
  • Artificial neural network
  • Machine learning
  • Prediction
  • Sepsis

ASJC Scopus subject areas

  • Health Policy
  • Health Informatics

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