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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Expert-augmented machine learning.

Efstathios D Gennatas1, Jerome H Friedman2, Lyle H Ungar3

  • 1Department of Radiation Oncology, University of California, San Francisco, CA 94143; gennatas@stanford.edu.

Proceedings of the National Academy of Sciences of the United States of America
|February 20, 2020
PubMed
Summary
This summary is machine-generated.

Expert-augmented machine learning (EAML) integrates clinician insights to refine predictive models. This approach identifies data issues and enhances model robustness, improving performance in critical applications.

Keywords:
computational medicinemachine learningmedicine

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Area of Science:

  • Artificial Intelligence
  • Clinical Informatics
  • Machine Learning

Background:

  • Machine learning model performance is constrained by data quality and quantity.
  • Trust in machine learning models is often limited, impacting adoption.
  • Combining human expertise with machine learning can optimize task performance.

Purpose of the Study:

  • To introduce expert-augmented machine learning (EAML), an automated method for integrating expert knowledge into machine learning models.
  • To assess the utility of EAML in identifying data issues and improving model performance using intensive care unit patient data.

Main Methods:

  • Developed EAML to extract and integrate expert knowledge into machine-learned models.
  • Derived 126 decision rules from intensive care patient data to predict hospital mortality.
  • Engaged 15 clinicians to assess the relative risk associated with each rule.
  • Compared clinician-assessed risk with empirical risk to identify discrepancies.

Main Results:

  • Clinician assessments revealed discrepancies with empirical data, highlighting potential training data issues like miscoded variables and hidden confounders.
  • Filtering rules based on clinician-data disagreement improved out-of-sample performance.
  • EAML enabled model training with reduced data requirements.

Conclusions:

  • EAML facilitates the automated creation of problem-specific priors, enhancing the robustness and dependability of machine learning models.
  • Integrating expert knowledge is crucial for identifying and rectifying data limitations in critical applications.
  • EAML offers a valuable framework for building trustworthy and high-performing AI in healthcare.