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Related Experiment Video

Updated: Jun 3, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Development and validation of a machine learning model predicting post-tonsillectomy hemorrhage.

Anker Stubberud1,2,3, Sverre Morten Zahl4, Tor Åge Myklebust5,6

  • 1Department of Otolaryngology, Helse Møre Og Romsdal Hospital Trust, Ålesund, Norway. anker.stubberud@ntnu.no.

European Archives of Oto-Rhino-Laryngology : Official Journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : Affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
|June 2, 2026
PubMed
Summary

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This summary is machine-generated.

Machine learning models can predict post-tonsillectomy hemorrhage with moderate accuracy. Key predictors include age, sex, and surgical hemostasis techniques, offering potential clinical decision-support.

Area of Science:

  • Otorhinolaryngology
  • Medical Informatics
  • Machine Learning

Background:

  • Post-tonsillectomy hemorrhage is a significant complication.
  • Predicting hemorrhage risk is crucial for patient management.

Purpose of the Study:

  • To develop and validate machine learning models for predicting post-tonsillectomy hemorrhage.
  • To identify key predictors of post-tonsillectomy bleeding.

Main Methods:

  • Analysis of a large cohort from the Norwegian tonsil registry (32,037 patients).
  • Development of supervised machine learning models using perioperative data.
  • Evaluation of model performance using Area Under the Curve (AUC) and SHAP plots.

Main Results:

Keywords:
Artificial intelligenceDecision curve analysisTonsilUnsupervised learning

Related Experiment Videos

Last Updated: Jun 3, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

  • The Adaboost classifier achieved an AUC of 0.71 for hemorrhage prediction.
  • Significant predictors included older age, male sex, and bipolar diathermy for hemostasis.
  • The model demonstrated superior performance compared to alternative strategies.

    • Conclusions:

    • Machine learning models can predict post-tonsillectomy hemorrhage with moderate accuracy.
    • Further research is needed to establish clinical utility as a decision-support tool.