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Related Experiment Video

Updated: Nov 9, 2025

Model Surgical Training: Skills Acquisition in Fetoscopic Laser Photocoagulation of Monochorionic Diamniotic Twin Placenta Using Realistic Simulators

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Published on: March 21, 2018

Understanding risk factors for postoperative mortality in neonates based on explainable machine learning technology.

Yaoqin Hu¹, Xiaojue Gong¹, Liqi Shu²

¹The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.

Journal of Pediatric Surgery

|April 17, 2021

Summary

An explainable machine learning model accurately predicts neonatal surgical mortality risk. This technology helps clinicians understand key risk factors, including vital signs during surgery, for improved patient outcomes.

Keywords:

Machine learning Neonatal surgery Postoperative mortality

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Area of Science:

Neonatal surgery
Machine learning in healthcare
Explainable artificial intelligence

Background:

Neonatal postoperative mortality presents a significant clinical challenge.
Understanding risk factors is crucial for improving surgical outcomes in neonates.

Purpose of the Study:

To introduce an explainable machine learning (ML) technology for identifying neonatal postoperative mortality risk factors.
To enhance clinician understanding of complex risk profiles in neonatal surgery.

Main Methods:

A cohort of 1481 neonatal surgeries was analyzed.
Machine learning models, including random forest, were trained to predict postoperative mortality.
SHAP (SHapley Additive exPlanations) was employed to interpret the best-performing model.

Main Results:

The random forest model achieved an area under the receiver operating characteristic curve of 0.72.
SHAP analysis identified key risk factors, including vital signs during surgery, and revealed additional factors beyond traditional statistical analysis.
Visualizations provided by SHAP enhanced model interpretability for clinicians.

Conclusions:

The explainable ML model demonstrated strong predictive performance for neonatal surgical mortality.
The technology facilitates a deeper understanding of individual patient risk factors, aiding clinical decision-making.