Abstract: A system may include a data store and a processor circuit in communication with the data store and a user input device. The data store may include clinical records associated with patients. For each of the patients, the clinical records may include data corresponding to a set of inputs associated with a medical condition of the patient and data corresponding to a set of outcomes associated with the medical condition of the patient. The processor circuit may be configured to obtain the clinical records via the data store and to receive, via the user input device, a selection of a driving outcome from among the set of outcomes. The processor circuit may be configured to determine a first ranking of the set of inputs based on the driving outcome and a classification model and to provide, at a display, a screen display including a graphical representation of the set of inputs automatically arranged based on the first ranking.

Abstract: The invention provides a method for evaluating a set of input data, the input data comprising at least one of: clinical data of a subject; genomic data of a subject; clinical data of a plurality of subjects; and genomic data of a plurality of subjects, using a deep learning algorithm. The method includes obtaining a set of input data, wherein the set of input data comprises raw data arranged into a plurality of data clusters and tuning the deep learning algorithm based on the plurality of data clusters. The deep learning algorithm comprises: an input layer; an output layer; and a plurality of hidden layers. The method further includes performing statistical clustering on the raw data using the deep learning algorithm, thereby generating statistical clusters and obtaining a marker from each statistical cluster. Finally, the set of input data is evaluated based on the markers to derive data of medical relevance in respect of the subject or subjects.

Abstract: The invention provides a method for evaluating a set of input data, the input data comprising at least one of: clinical data of a subject; genomic data of a subject; clinical data of a plurality of subjects; and genomic data of a plurality of subjects, using a deep learning algorithm. The method includes obtaining a set of input data, wherein the set of input data comprises raw data arranged into a plurality of data clusters and tuning the deep learning algorithm based on the plurality of data clusters. The deep learning algorithm comprises: an input layer; an output layer; and a plurality of hidden layers. The method further includes performing statistical clustering on the raw data using the deep learning algorithm, thereby generating statistical clusters and obtaining a marker from each statistical cluster. Finally, the set of input data is evaluated based on the markers to derive data of medical relevance in respect of the subject or subjects.

Abstract: A method of clustering or grouping subjects that are similar to one another. A dataset contains, for each subject, a set of quantitative values which each represent a respective clinical or pathological feature of that subject. A principal component analysis, PCA, is performed on the dataset. Loadings of one of the first two principal components identified by the PCA are used to generate a respective dataset of weighting values. These weighting values are used to weigh or modify each set of quantitative values in the dataset. A clustering algorithm is performed on the weighted sets of subject data. The process may be iterated until user-defined stopping conditions are satisfied.

Abstract: There is provided an apparatus and a method of operating the apparatus for providing feedback to a participant directing a communication to one or more other participants. The apparatus (100) comprises a processor (102) configured to acquire, from one or more physiological characteristic sensors (104), one or more physiological characteristic signals from at least one participant to which the communication is directed as the communication is received by the at least one participant. The processor (102) is also configured to determine a measure of the quality of the communication based on a comparison of the one or more physiological characteristic signals acquired from the at least one participant with one or more expected physiological characteristic signals and control a user interface (108) to provide feedback of the determined quality measure of the communication to the participant directing the communication to the at least one participant.

Abstract: A computer implemented method for generating a graphical representation of a predicted effectiveness of a first treatment. The method comprises using (102) a clinical model to determine at least one indicator related to an outcome of a first treatment. An effectiveness of the first treatment is then predicted (104) based on the at least one indicator. The predicted effectiveness of the first treatment is then displayed (106) to a user, using a first graphical representation.

Abstract: The invention discloses apparatus for performing a prognostic evaluation of a subject potentially having prostate cancer. The apparatus comprises a memory comprising instruction data representing a set of instructions; and a processor configured to communicate with the memory and to execute the set of instructions, wherein the set of instructions, when executed by the processor, cause the processor to obtain a subject profile (102) associated 5 with the subject; obtain clinical data (104) associated with the subject; obtain imaging data (106) acquired in respect of the subject's prostate; obtain pathological information (108) relating to a biopsy acquired in respect of the subject's prostate; determine, based on at least the subject profile, the clinical data, the imaging data and the pathological information, a prognostic score (110, 112) relating to the cancer. Computer-implemented methods and a 10 computer program product are also disclosed.

Abstract: The invention provides a method for evaluating a set of input data, the input data comprising at least one of: clinical data of a subject; genomic data of a subject; clinical data of a plurality of subjects; and genomic data of a plurality of subjects, using a deep learning algorithm. The method includes obtaining a set of input data, wherein the set of input data comprises raw data arranged into a plurality of data clusters and tuning the deep learning algorithm based on the plurality of data clusters. The deep learning algorithm comprises: an input layer; an output layer; and a plurality of hidden layers. The method further includes performing stabstical clustering on the raw data using the deep learning algorithm, thereby generating statistical clusters and obtaining a marker from each statistical cluster. Finally, the set of input data is evaluated based on the markers to derive data of medical relevance in respect of the subject or subjects.

Abstract: A method of clustering or grouping subjects that are similar to one another. A dataset contains, for each subject, a set of quantitative values which each represent a respective clinical or pathological feature of that subject. A principle component analysis, PCA, is performed on the dataset. Loadings of one of the first two principle components identified by the PCA are used to generate a respective dataset of weighting values. These weighting values are used to weigh or modify each set of quantitative values in the dataset. A clustering algorithm is performed on the weighted sets of subject data. The process may be iterated until user-defined stopping conditions are satisfied.

Abstract: There is provided an apparatus and a method of operating the apparatus for providing feedback to a participant directing a communication to one or more other participants. The apparatus (100) comprises a processor (102) configured to acquire, from one or more physiological characteristic sensors (104), one or more physiological characteristic signals from at least one participant to which the communication is directed as the communication is received by the at least one participant. The processor (102) is also configured to determine a measure of the quality of the communication based on a comparison of the one or more physiological characteristic signals acquired from the at least one participant with one or more expected physiological characteristic signals and control a user interface (108) to provide feedback of the determined quality measure of the communication to the participant directing the communication to the at least one participant.