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An opponent model for agent-based shared decision-making via a genetic algorithm.

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Summary
This summary is machine-generated.

This study introduces an enhanced shared decision-making (SDM) model using a genetic algorithm (GA) to predict patient and doctor preferences, improving treatment plan negotiation even with incomplete information.

Keywords:
agentauto-negotiationgenetic algorithmopponent modelshared decision-making (SDM)

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare
  • Decision Science

Background:

  • Shared decision-making (SDM) is crucial for patient-centered care but often hindered by time constraints and communication barriers.
  • Incomplete information environments reduce negotiation efficiency and patient-provider satisfaction in SDM.
  • Existing SDM models struggle with the complexities of patient and provider preferences.

Purpose of the Study:

  • To enhance the effectiveness of SDM by addressing information gaps.
  • To develop an auto-negotiation model for SDM that rapidly generates mutually satisfactory treatment plans.
  • To improve the efficiency and success rate of SDM negotiations.

Main Methods:

  • Integrated a genetic algorithm (GA) for opponent preference prediction into an SDM auto-negotiation model.
  • Constructed the SDM model using fuzzy constraints to handle uncertainty.
  • Simulated various negotiation scenarios to test the model's performance.

Main Results:

  • The proposed model demonstrated superior adaptation to multivariate negotiation scenarios compared to existing models.
  • The GA-enhanced SDM framework achieved higher mutual satisfaction rates.
  • Effective improvement in negotiation performance within incomplete information environments was observed.

Conclusions:

  • The agent negotiation framework supports personalized treatment plans, increasing patient satisfaction.
  • Incorporating GA-based preference prediction significantly enhances SDM negotiation in complex environments.
  • This approach offers a promising solution for optimizing patient-provider collaboration in healthcare.