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Efficient sepsis detection using deep learning and residual convolutional networks.

Ahmed S Almasoud1, Ghada Moh Samir Elhessewi2, Munya A Arasi3

  • 1Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Saudi Arabia.

Peerj. Computer Science
|September 24, 2025
PubMed
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This summary is machine-generated.

A new deep learning model combined with the African vulture optimization algorithm (AVOA) significantly improves sepsis detection accuracy. This advanced system offers a more reliable and timely diagnosis, crucial for patient survival and treatment effectiveness.

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Computational Biology

Background:

  • Sepsis is a life-threatening condition requiring prompt diagnosis and treatment.
  • Current clinical practices face challenges in timely sepsis detection.
  • Technological advancements are crucial for improving sepsis identification.

Purpose of the Study:

  • To develop a novel deep learning model for accurate sepsis detection.
  • To enhance model performance using the African vulture optimization algorithm (AVOA).
  • To improve timely intervention and patient outcomes in sepsis cases.

Main Methods:

  • Utilized an enhanced convolutional learning framework (ECLF) with atrous convolutions.
  • Incorporated a spatio-channel attention network (SCAN) for focused feature learning.
Keywords:
African vulture optimization algorithmDeep learningDilated convolutionEnhanced convolutional learningSepsis detectionSpatio-channel attention

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  • Employed a hierarchical dilated convolutional block (HDCB) and residual path convolutional chain (RPCC) for feature extraction and propagation.
  • Optimized the model using the African vulture optimization algorithm (AVOA).

    • Main Results:

    • Achieved high accuracy (99.4%), precision (98%), recall (99.2%), and F1-score (99.0%).
    • Demonstrated a superior area under the curve (AUC) of 0.998.
    • Outperformed traditional clinical scoring and conventional machine learning methods.

    Conclusions:

    • The proposed deep learning model with AVOA demonstrates superior accuracy and reliability in sepsis detection.
    • This approach facilitates timely intervention, potentially improving patient outcomes.
    • The model shows robustness and transferability for complex medical datasets.