Abstract: Described herein are solutions for providing a support to clinical decisions using a trained nonlinear classifier. For this purpose, a computer obtains (1202) for a patient (P) respective values (300) of a plurality of clinical data (p). Next, the computer generates a training dataset (306). For this purpose, the computer estimates (1204), by means of the nonlinear classifier, a respective class of risk (v) for the values of the patient (P) and adds the values of the patient (P) and the respective estimated class of risk (v) to the training dataset (306). The computer moreover generates (1406) a plurality of modified datasets (304) by modifying the values of the patient (P), estimates (1408) by means of the nonlinear classifier a respective class of risk (v) for each modified dataset (304), and adds the values of each modified dataset (304) and the respective estimated class of risk (v) to the training dataset (306).

Abstract: Solutions for estimating a variable of interest associated to a given disease as a function of omics data of a patient are provided. A first and second dataset of omics data is received. A multi-layer network is generated. Non-salient intra-omics connections and inter-omics connections of the multi-layer network are pruned. A plurality of communities of the multi-layer network are identified and one or more respective features are determined for each community. A training dataset is generated and a classifier configured for estimating the value of the variable of interest using the training dataset.

Abstract: Solutions for the prognosis of a given disease provided. A classifier is trained for estimating the index as a function of the respective set of features using a training dataset. For prognosis, further patient omics data is received. A verification dataset is generated by forming pairs of patients by combining the patient with the reference patients and determining, for each pair, a respective set of features. Estimation is performed through the classifier, for each pair of patients, of a respective index to generate an orderly list of the estimated indices. At each position in the list a respective parameter that indicates a probability is calculated. The position with the highest probability is selected and the disease-free survival time of the patient is estimated as a function of disease-free survival time of the reference patient in the selected position.

Abstract: The present disclosure pertains to a transmitting node (10) for a wireless communication network. The transmitting node (10) is adapted for transmission utilizing independently controllable antenna elements of an antenna arrangement (24). Moreover, the transmitting node (10) is adapted for switching between different beams and/or subsets of the antenna elements for transmission based on transmitted power in relation to EMF exposure limits. There are also disclosed related devices and methods.

Abstract: The present invention pertains to the field of wireless transmissions and presents exemplary methods for and designs of wireless communication devices. According to one example embodiment, there is provided of a wireless communication device (1), which comprises one or more proximity sensors (5), an antenna arrangement (3) and processing circuitry (7). The processing circuitry (7) is configured to obtain a sensor output (9) from the one or more proximity sensors. The processing circuitry (7) is further configured to control an operation of the antenna arrangement (3) to spatially steer electromagnetic energy transmitted from the antenna arrangement (3) based on the obtained sensor output (9). In exemplary embodiments, the processing circuitry (7) may be configured to control the operation of the antenna arrangement (3) to spatially steer the electromagnetic energy transmitted from the antenna arrangement (3) away from an obstructing object (2) whose proximity has been detected based the sensor output (9).

Abstract: A method for estimating a whole-body Specific Absorption Rate (SAR) caused in a body by electromagnetic fields emitted by a wireless communication device (40), where the body is represented by a phantom (30) and the wireless communication device (40) is positioned in the proximity of the phantom (30), comprises determining a complex electric field in a plurality of points distributed substantially in a single planar or curved surface (31) within the phantom (30), based on measurements of the magnitude of the electric field components in these points, and based on an assumption of constant phase of the electric field components. The method further comprises estimating a whole-body SAR in the phantom (30) based on the determined complex electric field in the plurality of points, and based on propagation of the complex electric field from the plurality of points into the volume of the phantom (30).