Identification and cause analysis on unplanned reoperations by text classification approach.

ABSTRACT: Unplanned reoperations (URs) not only increase the hospitalization period and healthcare cost, but also raise the death risk of patients. The analysis of URs is thus significant for their quality control and reduction. However, the massive text data generated in hospitals makes the identification of URs a tedious task with potential bias. Current research on UR is limited to data analysis and lack automated classification using deep learning and natural language processing. Here we propose the UR-Net framework. It implements the UR identification and cause analysis by processing the long texts of ward round documentation and applying few-shot learning on multi-class cause classification. Our framework consists of the URNet-XL with a batch fusion method based on XLNet model, and the URNet-G

SUBMITTER: Liang Z 

PROVIDER: S-EPMC12603117 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature