A computer-implemented method for training a model (M2) usable to compute an index of nociception (qNOX) related to a nociception effect during a general anesthesia procedure, the method comprising: obtaining, during a training phase separate from an actual use of the model (M2) during an anesthesia procedure, clinical data relating to a multiplicity of previous anesthesia procedures, deriving training data (TD) from said clinical data, deriving reference data (RD) from said clinical data, and training said model (M2) using said training data (TD) as input data to the model (M2) and said reference data (RD) as output data to the model (M2), wherein said training includes adjusting the model (M2) according to the training data (TD) and the reference data (RD), characterized in that the reference data (RD) is derived from the clinical data using an equation, RD = a ⋅ f 1 CeRemi + b ⋅ qCON + f 3 Resp including a mathematical term a·f1(CeRemi) whose value is non-linearly variable as a function of a concentration value relating to a drug concentration in a patient during an anesthesia procedure, wherein said function of the concentration value is an exponential function, causing low values of the concentration value to substantially increase the value of the reference data (RD), wherein a is a coefficient, and f1 defines said function, and CeRemi is said concentration value, wherein b is a coefficient, qCON is an index of consciousness, f3 is a function and Resp is a patient response parameter and wherein said model mathematical (M2) includes a set of coefficients for computing said index of nociception (qNOX) from input data derived from an encephalography signal (EEG), wherein during said training the coefficients are adjusted to define the model. obtaining, during a training phase separate from an actual use of the model (M2) during an anesthesia procedure, clinical data relating to a multiplicity of previous anesthesia procedures, deriving training data (TD) from said clinical data, deriving reference data (RD) from said clinical data, and training said model (M2) using said training data (TD) as input data to the model (M2) and said reference data (RD) as output data to the model (M2), wherein said training includes adjusting the model (M2) according to the training data (TD) and the reference data (RD), that the reference data (RD) is derived from the clinical data using an equation, RD = a ⋅ f 1 CeRemi + b ⋅ qCON + f 3 Resp including a mathematical term a·f1(CeRemi) whose value is non-linearly variable as a function of a concentration value relating to a drug concentration in a patient during an anesthesia procedure, wherein said function of the concentration value is an exponential function, causing low values of the concentration value to substantially increase the value of the reference data (RD), wherein a is a coefficient, and f1 defines said function, and CeRemi is said concentration value, wherein b is a coefficient, qCON is an index of consciousness, f3 is a function and Resp is a patient response parameter and wherein said model mathematical (M2) includes a set of coefficients for computing said index of nociception (qNOX) from input data derived from an encephalography signal (EEG), wherein during said training the coefficients are adjusted to define the model. <EOS>