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Development and Validation of an Interpretable Machine Learning Model for Predicting Adverse Clinical Outcomes in

Hongliang Li1, Yueyue Zhang2, Hangru Mei3

  • 1Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen 518000, China (H.L., Y.Y., L.W., X.C., K.W., H.L.).

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Summary

A new machine learning model accurately predicts adverse outcomes in placenta accreta spectrum (PAS) using MRI and clinical data. An online tool is now available to aid personalized PAS patient management.

Keywords:
Adverse Clinical OutcomesMachine LearningModel InterpretabilityPlacenta Accreta Spectrum

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Area of Science:

  • Medical imaging and diagnostics
  • Machine learning in healthcare
  • Perinatal medicine

Background:

  • Placenta accreta spectrum (PAS) is a severe pregnancy complication requiring precise risk identification.
  • Early detection of high-risk PAS patients is crucial for tailored treatment strategies.
  • Current diagnostic methods may benefit from advanced predictive modeling.

Purpose of the Study:

  • To develop and validate a machine learning model for predicting adverse outcomes in PAS.
  • To integrate MRI morphological indicators and clinical features for enhanced prediction accuracy.
  • To create an accessible online tool for real-time PAS risk assessment.

Main Methods:

  • Retrospective analysis of 125 PAS patients from two centers.
  • Development and validation of machine learning models (AdaBoost, TabPFN, CatBoost) using MRI and clinical data.
  • SHAP analysis for model interpretability and deployment via a web platform.

Main Results:

  • The CatBoost model demonstrated high performance with AUROCs of 0.90 (internal) and 0.84 (external validation).
  • Key predictors included cervical canal length, gestational age, prior C-sections, placental abnormal vasculature, and parturition.
  • An interpretable online tool providing real-time risk predictions and visualizations was developed.

Conclusions:

  • An interpretable and practical machine learning model for predicting adverse PAS outcomes was successfully developed.
  • The online prediction tool can support clinical decision-making for individualized PAS patient management.
  • This approach enhances the clinical applicability of predictive modeling in PAS.