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Related Experiment Videos

Machine Learning Methods for Precision Medicine Research Designed to Reduce Health Disparities: A Structured

Sanjay Basu1,2,3, James H Faghmous4, Patrick Doupe5

  • 1Research and Analytics, Collective Health, San Francisco, CA.

Ethnicity & Disease
|April 10, 2020
PubMed
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This tutorial guides researchers in using machine learning (ML) for precision medicine to reduce health disparities. It covers ML concepts, evaluation metrics, and common methods with R code examples.

Area of Science:

  • Computational biology
  • Health equity research
  • Biostatistics

Background:

  • Precision medicine aims to reduce health disparities by analyzing multi-level data.
  • Understanding disease manifestation and resource allocation in diverse populations is crucial.

Purpose of the Study:

  • To provide a structured tutorial on applying machine learning (ML) methods for precision medicine research focused on reducing health disparities.
  • To equip medical and public health researchers with practical knowledge and tools.

Main Methods:

  • Review of key machine learning concepts: supervised and unsupervised learning, regularization, cross-validation, bagging, and boosting.
  • Discussion of evaluation metrics for machine learners.
  • Overview of major learning approaches: tree-based, deep learning, and ensemble learning.
Keywords:
Deep LearningGradient Boosting MachinesHealth DisparitiesMachine LearningPrecision MedicineRandom Forest

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Main Results:

  • Highlights advantages and disadvantages of various ML approaches.
  • Provides strategies for interpreting "black box" models.
  • Demonstrates application of common ML methods using an example dataset with open-source R code.

Conclusions:

  • Machine learning offers powerful tools for precision medicine research aimed at mitigating health disparities.
  • The tutorial provides a practical framework for researchers to implement ML in their studies.
  • Accessible statistical code facilitates the application and reproducibility of these methods.