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Related Concept Videos

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Applying a community-engaged participatory machine learning model.

Emmanuella Ngozi Asabor1,2, Kammarauche Aneni3,4, Sitara Weerakoon5,6

  • 1Yale School of Medicine, New Haven, Connecticut, USA.

American Journal of Community Psychology
|September 16, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning in health care can perpetuate inequities. A community-engaged participatory model, prioritizing community insight, can mitigate bias and improve health outcomes by grounding algorithms in lived experiences.

Keywords:
bias in health carebiased algorithmscommunity‐based participatory researchmachine learningracism

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Area of Science:

  • Public Health
  • Community Psychology
  • Health Informatics

Background:

  • Predictive algorithms are proposed as solutions to healthcare bias.
  • Machine learning (ML) techniques risk perpetuating existing health inequities.
  • Community context is crucial for understanding and mitigating ML bias.

Purpose of the Study:

  • To propose a community-engaged participatory model for ML research in health.
  • To outline principles for mitigating bias in ML algorithms using community insights.
  • To foster equitable and effective ML applications in healthcare.

Main Methods:

  • Developing a community-engaged participatory model for ML research.
  • Integrating community insight at all research stages: priority setting, problem formulation, assumption definition, and interpretation.
  • Establishing guiding principles: shared decision-making, reflexivity and structural humility, flexibility and adaptability.

Main Results:

  • Community insight positions underrepresented groups as experts of their lived experiences.
  • Grounding ML in lived experiences ensures algorithms are ethically sound and effective.
  • Bidirectional partnerships between algorithmic scientists and communities are essential.

Conclusions:

  • A community-engaged participatory model is vital for unbiased ML in health.
  • Incorporating community stakeholders ensures algorithms are effective and ethically grounded.
  • This approach empowers communities and mitigates health inequities.