Abstract: Based on available genomic data for a patient, both a DNA language model and a protein language model are used to generate scores for medical decision support. These scores are used to predict the health of the patient. For example, decision support is provided by scoring expression of genes and the functionality of encoded proteins and inputting the scoring to a predictive model for predicting health of the patient.

Abstract: A computer-implemented method of adjudicating an imaged lesion, comprising: receiving a diagnostic image showing a lesion; processing the diagnostic image in a machine learning algorithm previously trained to classify the lesion and to propose, based on a lesion class for the lesion, a blood test panel suited to adjudicate the lesion; and outputting the proposed blood test panel to a user.

Abstract: A computer-implemented method for analyzing genomic sequence data comprises: obtaining genomic sequence data; obtaining data from three-dimensional protein structures; mapping the genomic sequence data on the protein structures; inputting the mapped genomic sequence data into a trained graph neural network; and deriving a diagnostic, prognostic and/or predictive conclusion output with respect to said disease or medical condition. The architecture of the graph neural network is based on the three-dimensional protein structure. The graph neural network is trained based on genomic sequence data from a cohort of subjects affected by a disease or medical condition mapped to the three-dimensional protein structures and corresponding diagnostic, prognostic and/or predictive conclusions in the context of the disease or medical condition.

Abstract: One or more example embodiments relates to a computer-implemented method for determining a similarity measure, the similarity measure describing a similarity between a first patient and a second patient. The method includes receiving a first patient data record, wherein the first patient data record is assigned to the first patient; receiving a second patient data record, wherein the second patient data record is assigned to the second patient; receiving or determining a medical ontology, wherein the medical ontology is independent of the first patient data record and the second patient data record; determining a patient ontology based on the medical ontology and at least one of the first patient data record or the second patient data record; and determining the similarity measure based on the patient ontology.

Abstract: Systems and methods for determining an assessment of a patient for a medical condition are provided. Input medical data of a patient is received. A knowledge graph is computed based on the input medical data. A vector representing a state of the patient is generated based on the knowledge graph. An assessment of the patient for a medical condition is determined using a machine learning based network based on the vector. The assessment of the patient is output.

Abstract: Systems and methods for determining an assessment of a patient for a medical condition are provided. Input medical data of a patient is received. A vector representing a state of the patient is generated based on the input medical data. An assessment of the patient for a medical condition is determined using a machine learning based network based on the vector. The assessment of the patient is output.

Abstract: Disclosed are methods of predicting a radiotherapy success in a method of treating a lung cancer of a patient, the use of specific markers for predicting a radiotherapy success in a method of treating a lung cancer of a patient, a database comprising the markers, and a computer program product for use in such a method.

Abstract: Radio communication resources are allocated in an at least partially self-organising radio communications system having several user stations and at least one central entity for organizing the allocation of radio communication resources, in addition to a corresponding radio communications system. Resources for direct communication between at least two respective user stations are allocated repeatedly at least partly by the central entity.