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Pilot relaying is a type of differential protection used in power systems. It compares electrical quantities at the terminals of equipment via a communication channel instead of direct relay interconnection. This method is essential for transmission lines where the terminals are far apart, typically up to 80 km for lines with 69 to 115 kV ratings. Four types of communication channels are used for pilot relaying:
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Related Experiment Video

Updated: Jan 30, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Machine Learning Can Improve Estimation of Surgical Case Duration: A Pilot Study.

Justin P Tuwatananurak1, Shayan Zadeh2, Xinling Xu1

  • 1Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA.

Journal of Medical Systems
|January 19, 2019
PubMed
Summary

A new machine learning algorithm significantly improved predicted case duration accuracy in operating rooms. This surgical scheduling advancement reduced overall inaccuracy by 70%, enhancing hospital efficiency.

Keywords:
Case durationEfficiencyModelNatural language processingOperating roomPrediction

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Area of Science:

  • Healthcare Operations
  • Artificial Intelligence in Medicine
  • Surgical Workflow Optimization

Background:

  • Accurate operating room (OR) utilization is crucial for hospital profitability.
  • Surgical scheduling relies on precise predictions of operating room case duration.
  • Traditional methods for predicting case duration include surgeon experience and electronic health record (EHR) averages.

Purpose of the Study:

  • To compare the predicted case duration (pCD) accuracy of a novel machine-learning algorithm against a conventional EHR.
  • To evaluate the impact of machine learning on surgical scheduling and OR efficiency.

Main Methods:

  • A proprietary machine learning algorithm was developed, incorporating patient demographics, pre-surgical milestones, and hospital logistics.
  • Predicted case duration (pCD) from the machine learning algorithm (Leap Rail) was compared to a conventional EHR over a 3-month period.
  • Actual case duration was recorded from patient entry to exit from the OR; pCD accuracy was measured by the absolute difference between predicted and actual times.

Main Results:

  • The machine learning algorithm demonstrated a 7-minute improvement in absolute difference between pCD and actual case duration compared to the EHR (p < 0.0001).
  • The Leap Rail method achieved a 70% reduction in overall surgical scheduling inaccuracy.
  • The study analyzed 990 eligible surgical cases from a total of 1059 performed.

Conclusions:

  • Machine learning algorithms offer a promising approach to enhance pCD accuracy.
  • Improved pCD accuracy through machine learning can significantly optimize OR planning and hospital efficiency.