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Background <p>Due to the high mortality rate of sepsis, timely identification and intervention in the intensive care unit are essential to improve patient outcomes. Accurate prediction models are critical in addressing the challenge of early sepsis detection.</p> Methods <p>In this study, we designed a prediction model, KA-Transformer, based on time series data to achieve advance prediction of sepsis. The kernel attention mechanism is introduced in KA-Transformer, which significantly optimizes the problems of limited training samples, many parameters, and uneven sample distribution.</p> Results <p>By inputting continuous time-series data, the model predicted the area under the wok characteristic curve for subjects with sepsis to be 0.962, 0.944, and 0.984 at the three prediction time points of 1, 6, and 12&#xa0;h before the onset of sepsis, with accuracies of 92.3%, 93.9%, and 96.1%, respectively.</p> Conclusions <p>The experimental results show that KA-Transformer outperforms existing methods in terms of accuracy and generalization ability for sepsis prediction, and has the potential to achieve continuous prediction and improve the real-time and reliability of prediction.</p>

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Application of the KA-Transformer model to early sepsis prediction: a hybrid network analysis based on time series data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月12日

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  • Yu Zhu,
  • Yunzhang Cheng,
  • Tianyi Zhang,
  • Li Zhang,
  • Xinjie Hong,
  • Dongmei Wang,
  • Jian Xiao

摘要

Background

Due to the high mortality rate of sepsis, timely identification and intervention in the intensive care unit are essential to improve patient outcomes. Accurate prediction models are critical in addressing the challenge of early sepsis detection.

Methods

In this study, we designed a prediction model, KA-Transformer, based on time series data to achieve advance prediction of sepsis. The kernel attention mechanism is introduced in KA-Transformer, which significantly optimizes the problems of limited training samples, many parameters, and uneven sample distribution.

Results

By inputting continuous time-series data, the model predicted the area under the wok characteristic curve for subjects with sepsis to be 0.962, 0.944, and 0.984 at the three prediction time points of 1, 6, and 12 h before the onset of sepsis, with accuracies of 92.3%, 93.9%, and 96.1%, respectively.

Conclusions

The experimental results show that KA-Transformer outperforms existing methods in terms of accuracy and generalization ability for sepsis prediction, and has the potential to achieve continuous prediction and improve the real-time and reliability of prediction.