Abstract: Provided is a method for the model-based determination of a system status of a dynamic system by means of a model, wherein: a recurrent neural network is provided as the model of the dynamic system; the model is supplied with a time series of potentially recordable measurement values as an input variable, the values comprising recorded and missing measurement values; at least one system status associated with a time point is generated from the model, from which status at least one target value belonging to the respective time point can be determined; sequential system statuses transition into one other by means of a respective status transition; and a correction of at least one system status is carried out on the basis of the time series with the aid of the status transition.

Abstract: The invention relates to a method for determining a tissue type of a tissue of an animal or human individual, in which method: electromagnetic radiation (26) emitted by a tissue sample (24) of the tissue is sensed (10) by means of a radiation sensor (22), the radiation sensor (22) providing a sensor signal (28) in accordance with the sensed electromagnetic radiation, and the sensor signal (28) is evaluated (12) by means of an evaluation unit (30) in order to determine and output the tissue type. The problem addressed by the invention is that of enabling improved determination of the tissue type. According to the invention, the evaluation unit (30) is a self-learning evaluation unit (30) that is initially trained (14) by means of training data sets (32) on the basis of at least one model, which is based on a method for machine learning, the training of the evaluation unit being conducted by means of such training data sets (32) each comprising a training sensor signal with an associated training tissue type.

Abstract: A biochemical analytical device and a biochemical analytical method for determining an analyte in a test sample are provided. In the technique, the biochemical analytical device includes a sample port to receive the test sample, a sensor to probe the test sample and to generate sensor data, and a processor. The sensor data corresponds to the analyte in the test sample. The processor receives the sensor data from the sensor and selects a non-linear function for the received sensor data. The processor fits the selected non-linear function to the sensor data. Additionally, the processor compares the fitted non-linear function to a reference data to determine the analyte in the test sample.