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

Anmar Abdul-Rahman, William Morgan, Aleksandar Vukmirovic and Dao-Yi Yu

PLOS ONE, 2024, vol. 19, issue 7, 1-20

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

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0306028

DOI: 10.1371/journal.pone.0306028

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