A medical data processing method for determining an indicator relating to an injury of an anatomical structure of a patient, wherein the anatomical structure is the head of the patient, in particular the brain, wherein the injury is a concussion, swelling, fracture or rupture of the anatomical structure or internal bleeding, wherein the indicator gives an indication whether the injury is present or not, wherein the method comprises executing, on at least one processor of at least one computer, steps of: a) acquiring acceleration data describing an energy of a set of one or more signals in dependence on both time and frequency, the set of signals acquired by measuring the acceleration of the anatomical structure over time; b) acquiring analysis data describing an analysis rule for determining at least two of b1) an overall energy level of at least one signal of the set of signals, presenting parameter1, b2) a correlation between at least two signals of the set of signals in the frequency domain, the at least two signals respectively measured at at least two different respective regions of the anatomical structure presenting parameter2, or b3) a relationship between energies given for at least two different frequency ranges of at least one signal of the set of signals presenting parameter3; c) determining indicator data describing the indicator based on the acceleration data and the analysis data, characterized in that the indicator data are determined by means of a machine learning algorithm using a plurality of classified acceleration data acquired for a first population of patients having the injury and a second population of patients not having the injury, wherein the machine learning algorithm is a support vector machine, a random forest or a neural network, wherein the analysis data is used to train the machine learning algorithm, wherein the machine learning algorithm selects and combines parameter1, parameter2 and/or parameter3, such that the best possible discrimination power between the first population of patients having the injury and the second population of patients not having the injury is achieved. a) acquiring acceleration data describing an energy of a set of one or more signals in dependence on both time and frequency, the set of signals acquired by measuring the acceleration of the anatomical structure over time; b) acquiring analysis data describing an analysis rule for determining at least two of b1) an overall energy level of at least one signal of the set of signals, presenting parameter1, b2) a correlation between at least two signals of the set of signals in the frequency domain, the at least two signals respectively measured at at least two different respective regions of the anatomical structure presenting parameter2, or b3) a relationship between energies given for at least two different frequency ranges of at least one signal of the set of signals presenting parameter3; b1) an overall energy level of at least one signal of the set of signals, presenting parameter1, b2) a correlation between at least two signals of the set of signals in the frequency domain, the at least two signals respectively measured at at least two different respective regions of the anatomical structure presenting parameter2, or b3) a relationship between energies given for at least two different frequency ranges of at least one signal of the set of signals presenting parameter3; c) determining indicator data describing the indicator based on the acceleration data and the analysis data, characterized in that the indicator data are determined by means of a machine learning algorithm using a plurality of classified acceleration data acquired for a first population of patients having the injury and a second population of patients not having the injury, wherein the machine learning algorithm is a support vector machine, a random forest or a neural network, wherein the analysis data is used to train the machine learning algorithm, wherein the machine learning algorithm selects and combines parameter1, parameter2 and/or parameter3, such that the best possible discrimination power between the first population of patients having the injury and the second population of patients not having the injury is achieved. characterized in that the indicator data are determined by means of a machine learning algorithm using a plurality of classified acceleration data acquired for a first population of patients having the injury and a second population of patients not having the injury, wherein the machine learning algorithm is a support vector machine, a random forest or a neural network, wherein the analysis data is used to train the machine learning algorithm, wherein the machine learning algorithm selects and combines parameter1, parameter2 and/or parameter3, such that the best possible discrimination power between the first population of patients having the injury and the second population of patients not having the injury is achieved.