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Related Experiment Video

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Emergency Undocking in Robotic Surgery: A Simulation Curriculum
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Risk prediction in surgery using case-based reasoning and agent-based modelization.

Bruno Perez1, Christophe Lang1, Julien Henriet1

  • 1FEMTO-ST Institute, Univ. Bourgogne-Franche-Comt é, CNRS, DISC, 16 Route de Gray, 25030, Besan çon, France.

Computers in Biology and Medicine
|November 16, 2020
PubMed
Summary

This study introduces a novel system combining Multi-Agent Systems (MAS) and Case-Based Reasoning (CBR) to dynamically set operating room alert thresholds. The developed model effectively manages diverse data, outperforming predefined alerts for improved surgical safety.

Keywords:
Adverse events in surgeryCase-based reasoningMulti-agent systemRisk predictionSimulation

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Surgical Safety Systems

Background:

  • Managing operating room risks is critical, with a key challenge being the definition of alert thresholds in unpredictable, non-deterministic environments.
  • Existing alert systems often rely on predefined thresholds, which may not be optimal for managing diverse and dynamic adverse events during surgery.

Purpose of the Study:

  • To present a novel architecture coupling a Multi-Agent System (MAS) with Case-Based Reasoning (CBR) for adaptive alert threshold management in operating rooms.
  • To evaluate the efficacy of this MAS-CBR model in determining dynamic alert thresholds using simulated diverse data, including infectious agents, patient vitals, and human fatigue.
  • To compare the performance of different similarity calculation methods within the CBR component.

Main Methods:

  • Development of a hybrid architecture integrating a Multi-Agent System (MAS) for situation emulation and Case-Based Reasoning (CBR) for analytical data management.
  • Simulation of a large number of operating room scenarios to train and test the MAS-CBR model.
  • Comparative analysis of various similarity metrics for the CBR's retrieval phase to optimize threshold determination.

Main Results:

  • The proposed MAS-CBR model successfully managed alert thresholds across disparate data types, including infectious agents, patient vitals, and human fatigue.
  • Alert thresholds generated by the system demonstrated superior efficiency compared to conventionally predefined thresholds.
  • The study confirmed the system's effectiveness as an alert generator in a simulated environment.

Conclusions:

  • The coupled MAS-CBR architecture offers an original and efficient approach to defining dynamic alert thresholds in complex operating room environments.
  • The system's ability to adapt to varied data suggests significant potential for enhancing surgical safety and real-time risk management.
  • Future work will focus on integrating real-world data from monitoring sensors to further validate and enrich the simulation model.