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Precision medicine and the cursed dimensions.

Dennis L Barbour1

  • 1Departments of Biomedical Engineering, Computer Science and Engineering, Neuroscience and Otolaryngology, Washington University in St. Louis, St. Louis, MO 63130 USA.

NPJ Digital Medicine
|July 16, 2019
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Summary

The curse of dimensionality challenges precision medicine by making it harder to average patient data across many factors. This limits the effectiveness of evidence-based treatments and requires new strategies like predictive observation models.

Keywords:
Diagnostic markersTranslational research

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Area of Science:

  • Medical Informatics
  • Biostatistics
  • Computational Biology

Background:

  • Human intuition of 'average' is based on one-dimensional thinking.
  • This intuition fails in higher dimensions where individuals rarely match the average across multiple measurements.
  • This phenomenon is known as the curse of dimensionality.

Purpose of the Study:

  • To explain the curse of dimensionality and its impact on precision medicine.
  • To highlight the limitations imposed by high-dimensional data in medical diagnosis and treatment.
  • To introduce predictive observation models as a strategy to mitigate these limitations.

Main Methods:

  • Conceptual analysis of the curse of dimensionality in a medical context.
  • Discussion of the challenges in averaging multi-factorial patient data for disease classification.
  • Exploration of predictive observation models as a compensatory strategy.

Main Results:

  • The curse of dimensionality inherently limits the ability to precisely classify patients and treatments in high-dimensional medical data.
  • Increased diagnostic sophistication with more predictive factors exacerbates the problem, making disease classes more dissimilar.
  • Failure to account for this phenomenon restricts the potential of precision medicine.

Conclusions:

  • The curse of dimensionality poses a fundamental challenge to achieving the full potential of precision medicine.
  • Integrating predictive observation models into patient workups is a viable strategy to overcome these high-dimensional data limitations.
  • Addressing the curse of dimensionality is crucial for advancing evidence-based medicine and personalized treatment selection.