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: Disclosed herein is a medical system (100, 300, 500) comprising: a memory (110) storing machine executable instructions, at least one set of predetermined coordinates (124), and a position identifying algorithm (122). The position identifying algorithm is configured for outputting a set of current coordinates (128) for each of the at least one set of predetermined coordinates in response to receiving a current image descriptive of an object (306, 310). The execution of machine executable instructions (120) causes a computational system (104) to repeatedly receive (200) a current image (126) from a camera system (304).

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: The present disclosure pertains to a system for providing model-based predictions of quality of life implications of a treatment via individual-specific machine learning models. In some embodiments, the system (i) obtains social media information related to an individuals social media activities, the individual awaiting prescription of a predetermined treatment; (ii) performs one or more queries to obtain health and social media information associated with similar individuals having (a) similar social media activities and (b) a record of receiving the predetermined treatment; (iii) provides the health and social media information associated with the similar individuals to a machine learning model to train the machine learning model; and (iv) provides the social media information associated with the individual to the machine learning model to predict (a) a likelihood of the one or more health states and (b) an estimated importance of each of the one or more health states for the individual.

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: According to an aspect, there is provided a method of selecting a criterion for determining which subjects from a plurality of subjects to include in a medical trial. The method comprises, for a dataset comprising one or more entries, for each of the plurality of subjects: obtaining a plurality of test criteria; determining, for each test criterion, a measure of how evenly the entries in the dataset are distributed between satisfying the test criterion and not satisfying the test criterion; and selecting a criterion from the plurality of test criteria based on the determined measures. A computer program product is 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.

Abstract: A method and corresponding system for increasing the likelihood of inducing behavior change in a lifestyle management program for a user includes sensing at least one behavior parameter of a user; identifying at least one intention-behavior gap based on the sensing of the behavior parameter; via application of a genetic algorithm generating a quantified profile of the intention-behavior gap; via a user interface, suggesting at least one action to accept or reject; and varying the quantified profile based on the action accepted or rejected by the user. The user interface may include a smart phone that includes at least one sensor for sensing location and activity of a user of the user interface, or a dedicated device that communicates with a sensor for sensing a behavior parameter, or the sensor is disposed on the body of the user or on a garment worn by the user.

Abstract: The present invention relates to a device, system and method for assessing the information needs of a person.

Abstract: A method and corresponding system for increasing the likelihood of inducing behavior change in a lifestyle management program for a user includes sensing at least one behavior parameter of a user; identifying at least one intention-behavior gap based on the sensing of the behavior parameter; via application of a genetic algorithm generating a quantified profile of the intention-behavior gap; via a user interface, suggesting at least one action to accept or reject; and varying the quantified profile based on the action accepted or rejected by the user. The user interface may include a smart phone that includes at least one sensor for sensing location and activity of a user of the user interface, or a dedicated device that communicates with a sensor for sensing a behavior parameter, or the sensor is disposed on the body of the user or on a garment worn by the user.

Abstract: A method for coaching a subject, including receiving data corresponding to at least one of a desired behavior of the subject or an undesired behavior of the subject, receiving a level of available self control of the subject via an input source, calculating a level of required self control needed to perform the desired behavior or suppress the undesired behavior, analyzing a risk based on the level of available self control and the level of required self control, where the risk is associated with predicting whether the subject will perform the undesired behavior or not perform the desired behavior, and intervening when the risk analyzed is above a particular threshold.

Abstract: A method and corresponding system for increasing the likelihood of inducing behavior change in a lifestyle management program for a user includes sensing at least one behavior parameter of a user; identifying at least one intention-behavior gap based on the sensing of the behavior parameter; via application of a genetic algorithm generating a quantified profile of the intention-behavior gap; via a user interface, suggesting at least one action to accept or reject; and varying the quantified profile based on the action accepted or rejected by the user. The user interface may include a smart phone that includes at least one sensor for sensing location and activity of a user of the user interface, or a dedicated device that communicates with a sensor for sensing a behavior parameter, or the sensor is disposed on the body of the user or on a garment worn by the user.