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Related Experiment Video

Updated: Jan 9, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

Machine Learning-Based Demand Prediction for Emergency Medical Services.

Lorenzo L Gianquintieri, Eleonora E Sala, Enrico Gianluca E G Caiani

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Machine learning significantly improves emergency medical services (EMS) demand forecasting by integrating environmental factors. This enhances resource optimization and reduces computational time for better out-of-hospital patient care.

    Related Experiment Videos

    Last Updated: Jan 9, 2026

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
    04:09

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

    Published on: October 10, 2018

    8.6K

    Area of Science:

    • Operations Research
    • Data Science
    • Public Health

    Background:

    • Emergency Medical Services (EMS) are vital for out-of-hospital care.
    • Optimizing EMS operations requires accurate demand prediction and resource allocation.
    • Existing digital twins for EMS lack efficiency and integration of temporal factors like environmental conditions.

    Purpose of the Study:

    • To enhance an existing EMS digital twin using machine learning for improved demand forecasting.
    • To integrate external time-variant factors and reduce computational processing time.
    • To enable more efficient EMS deployment and resource optimization.

    Main Methods:

    • Development and comparison of multiple machine learning models.
    • Integration of external time-variant factors into the predictive model.
    • Evaluation of model performance against historical data and a baseline using error metrics (RMSE).

    Main Results:

    • The machine learning-enhanced model significantly improved predictive accuracy, reducing RMSE from 8.7 to 2.5 events/hour.
    • Substantial reduction in computational time, enabling day-by-day predictions.
    • Demonstrated potential for real-time decision-making in EMS resource allocation.

    Conclusions:

    • Machine learning offers a more efficient and accurate approach to EMS demand forecasting.
    • The enhanced model provides valuable insights for policy development and resource alignment with demand dynamics.
    • This approach can lead to improved emergency response outcomes and better out-of-hospital patient care.