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Predicting Postoperative Outcomes in Lower Gastrointestinal Surgery: A Machine Learning Approach Using Electronic

Lu-Yen Anny Chen, Shu-Yi Wang, Shu-Chuan Amy Lin

|December 26, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) can predict surgical risks. Machine learning models identified patient age and diagnoses as key factors for predicting postoperative complications and hospital stay length in lower gastrointestinal surgery.

Keywords:

Support Vector Machine (SVM) model artificial intelligence (AI) in health care machine learning (ML)postoperative complications risk prediction

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Area of Science:

Surgical care
Artificial intelligence
Machine learning

Background:

Surgical care is increasingly complex.
Artificial intelligence (AI) presents opportunities to enhance patient outcomes.
Predictive modeling can aid in managing surgical risks.

Purpose of the Study:

To investigate the use of machine learning (ML) for predicting postoperative complications.
To predict the length of hospital stay in patients undergoing lower gastrointestinal surgery.
To identify significant preoperative factors influencing surgical outcomes.

Main Methods:

Analysis of operative electronic health records from 771 patients.
Application of Support Vector Machine (SVM) models for prediction.
Cross-validation was used to assess model performance.

Main Results:

Preoperative factors like age and number of diagnoses significantly predict postoperative complications.
An increased number of diagnoses correlates with higher complication rates and longer hospital stays.
The SVM model achieved 75.56% cross-validation accuracy in predicting complications.

Conclusions:

AI and ML models show potential for improving perioperative care.
Predictive models can facilitate early risk identification and personalized patient management.
Further research is needed to refine AI predictive models for nursing practice.