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Probability density and information entropy of machine learning derived intracranial pressure predictions

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  • Anmar Abdul-Rahman
  • William Morgan
  • Aleksandar Vukmirovic
  • Dao-Yi Yu

Abstract

Even with the powerful statistical parameters derived from the Extreme Gradient Boost (XGB) algorithm, it would be advantageous to define the predicted accuracy to the level of a specific case, particularly when the model output is used to guide clinical decision-making. The probability density function (PDF) of the derived intracranial pressure predictions enables the computation of a definite integral around a point estimate, representing the event’s probability within a range of values. Seven hold-out test cases used for the external validation of an XGB model underwent retinal vascular pulse and intracranial pressure measurement using modified photoplethysmography and lumbar puncture, respectively. The definite integral ±1 cm water from the median (DIICP) demonstrated a negative and highly significant correlation (-0.5213±0.17, p

Suggested Citation

  • Anmar Abdul-Rahman & William Morgan & Aleksandar Vukmirovic & Dao-Yi Yu, 2024. "Probability density and information entropy of machine learning derived intracranial pressure predictions," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-20, July.
    • Handle: RePEc:plo:pone00:0306028
    DOI: 10.1371/journal.pone.0306028
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