Computing methods

This page presents the various computing methods deployed within EzeCHiel.

These computing methods are decomposed into the following categories:

  1. Prediction engines, to calculate the predicted concentration from the patient/population parameters
  2. A posteriori engines, to calculate the a posteriori parameters of a patient, based on the observed concentration and the population data
  3. Posology engines, to offer propositions about suitable posologies, considering a set of constraints (time, available doses)

Prediction engines

Currently, two main models have been implemented, both of them for 1, 2 and 3 compartments, and for different kind of intakes:

  1. Linear elimination
  2. Michaelis-Menten elimination

Intakes supported by EzeCHiel:

  1. Bolus
  2. Infusion
  3. Extravascular

The equations of these prediction engines are detailed within 6 subpages, corresponding to the type of elimination and number of compartments:

Linear elimination

Michaelis-Menten elimination (non-linear)

A posteriori engines

Bayesian approach


Posology engines