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Related Experiment Video

Updated: Jan 11, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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Identification and cause analysis on unplanned reoperations by text classification approach.

Zhancheng Liang1, Wenyang Huang1, Hongyu Xu1

  • 1Shensi Lab, Shenzhen Institute for Advanced Study, UESTC, Shenzhen, China.

Scientific Reports
|November 10, 2025
PubMed
Summary

Unplanned reoperations (URs) pose risks and increase costs. This study introduces UR-Net, an automated framework using deep learning to identify URs and their causes from clinical notes, improving patient care.

Keywords:
Medical quality controlMedical textText classificationUnplanned reoperation

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Last Updated: Jan 11, 2026

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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Data Analysis

Background:

  • Unplanned reoperations (URs) significantly increase patient mortality, hospitalization duration, and healthcare expenses.
  • Manual identification of URs from extensive clinical documentation is labor-intensive, prone to bias, and lacks efficiency.
  • Existing research lacks automated classification methods for URs using deep learning and natural language processing.

Purpose of the Study:

  • To develop and evaluate an automated framework, UR-Net, for identifying unplanned reoperations (URs) and classifying their causes from ward round documentation.
  • To leverage deep learning and natural language processing techniques for efficient and unbiased UR analysis.
  • To provide a scalable solution for quality control and reduction of URs in healthcare settings.

Main Methods:

  • The UR-Net framework integrates UR identification (URNet-XL) and cause classification (URNet-GT).
  • URNet-XL employs a batch fusion method based on the XLNet model for processing long clinical texts.
  • URNet-GT utilizes a pre-trained model with multi-head attention and bi-directional Gated Recurrent Unit for multi-class cause classification, incorporating few-shot learning.

Main Results:

  • The UR-Net framework achieved high weighted F1 scores of 96.34% for UR identification and 93.37% for cause classification, outperforming baseline methods.
  • The Area Under the receiver operating characteristic Curve (AUC) reached 97.86%, demonstrating excellent classification performance on an imbalanced dataset.
  • The proposed model shows significant potential for accurate and automated analysis of unplanned reoperations.

Conclusions:

  • The UR-Net framework offers a novel, automated approach for identifying unplanned reoperations and their underlying causes from unstructured clinical text.
  • This deep learning-based method enhances the efficiency and accuracy of UR analysis compared to traditional manual methods.
  • The successful implementation of UR-Net provides a pathway for reducing the incidence of unplanned reoperations and improving patient outcomes.