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A reinforcement learning-guided interpretable method for postoperative sepsis prediction with Hilbert-Schmidt

Kunhua Zhong1, Han Chen2, Qilong Sun1

  • 1Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China.

Frontiers in Big Data
|April 23, 2026
PubMed
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This study introduces a new AI framework for predicting sepsis after surgery using electronic health records (EHR). The interpretable model achieves high accuracy with fewer features, improving efficiency and clinical understanding.

Area of Science:

  • Artificial Intelligence in Medicine
  • Machine Learning for Healthcare
  • Clinical Informatics

Background:

  • Sepsis poses a significant threat to patient outcomes post-surgery, necessitating accurate and efficient risk stratification.
  • Current sepsis prediction models often lack transparency and rely on computationally intensive data streams.
  • Electronic Health Records (EHR) offer rich data but present challenges in processing high-dimensional, heterogeneous information.

Purpose of the Study:

  • To develop a reinforcement learning-guided framework for interpretable feature engineering in postoperative sepsis prediction.
  • To enhance the accuracy and computational efficiency of sepsis risk stratification using perioperative data.
  • To provide clinically interpretable insights into sepsis prediction models.

Main Methods:

Keywords:
Hilbert–Schmidt Independence Criterionfeature engineeringlarge-scale medical datareinforcement learningself-attentionsepsistemporal convolutional networks

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  • An Actor-Critic reinforcement learning approach was employed, treating feature selection as an action.
  • A self-attention-based classifier was utilized for downstream prediction.
  • An auxiliary baseline model incorporating temporal convolutional networks (TCN) and Hilbert-Schmidt Independence Criterion (HSIC) regularization was introduced for benchmarking.

Main Results:

  • The proposed framework achieved performance comparable to or exceeding existing machine learning baselines on a real-world surgical cohort.
  • The model demonstrated superior efficiency by selecting a reduced set of input features.
  • Perfect scores (1.00) for F1-score, Sensitivity, and Specificity were attained on the experimental dataset.

Conclusions:

  • The developed method accurately predicts postoperative sepsis with enhanced interpretability.
  • The framework offers a novel and efficient approach to postoperative sepsis prediction using perioperative EHR data.
  • Instance-level explanations enhance the clinical utility and trustworthiness of the sepsis prediction model.