Abstract: According to an aspect, there is provided a computer-implemented method (100) for assessing a subject's adherence to a treatment for a condition, the method comprising receiving (102) adherence data indicative of the subject's past adherence to the treatment; receiving (104) medical data indicative of physiological details and a medical history of the subject; determining (106), based on the received adherence data, a non-adherence risk score indicative of a likelihood that the subject will not adhere to the treatment within a defined time period in the future; determining (108), based on the medical data, an adverse event risk score indicative of a likelihood that the subject will experience an adverse medical event; determining (110), based on the non-adherence risk score and the adverse event risk score, a priority classification to be assigned to the subject; and generating (122), based on the priority classification, an instruction signal to be delivered to a recipient

Abstract: The present invention relates to a method for screening cardiac conditions of a patient, the method comprising the following steps: calculating (S1) a cardiac risk value based on therapy sensor data by means of a first level of a multi-level procedure, wherein the therapy sensor data is provided from a first data source as sensor data during a therapy of the patient; and refining (S2) the calculated cardiac risk value based on survey data and/or device data by means of a second level of a multi-level procedure to provide a refined cardiac risk value, wherein the survey data and/or the device data is provided from a second data source by incremental data gathering.

Abstract: A system for detecting clinical deterioration in a patient having a health condition during one or more post-discharge patient follow-up periods is provided. The system includes a computer system that has one or more physical processors programmed with computer program instructions which, when executed cause the computer system to: determine a clinical deterioration risk detection model for each of the post-discharge patient follow-up periods, and determine a clinical deterioration risk detection value for the patient in the post-discharge patient follow-up period using the corresponding clinical deterioration risk detection model. The clinical deterioration risk detection model is a function of health information of the patient in the corresponding post-discharge patient follow-up period. The clinical deterioration risk detection value is configured to predict the likelihood of the clinical deterioration in the patient in the post-discharge patient follow-up period.

Abstract: There is provided a method of processing measurements of acceleration to identify steps by a user, the method comprising obtaining measurements of acceleration from a device worn or carried by a user; analysing the measurements of acceleration to determine a value for a threshold that is to be used to identify steps by the user in measurements of acceleration; and using the determined value for the threshold to identify steps by the user in measurements of acceleration.

Abstract: The present disclosure relates to a conversation facilitation system and method configured for capturing and presenting life moment information for a subject with cognitive impairment. The system and method comprises obtaining life moment information from one or more life moment capturing devices. The life moment information comprises information on daily activities experienced by the subject, including locations of daily activities and/or co-participants in the daily activities. The system and method comprises storing the life moment information for later review by the subject and/or a caregiver of the subject. The system and method comprises obtaining external data relating to the life moment information from one or more external data sources. The system and method comprises utilizing the life moment information and the external data to generate a life moment timeline.

Abstract: The present disclosure pertains to a system configured to detect slow wave sleep and/or non-slow wave sleep in a subject during a sleep session based on a predicted onset time of slow wave sleep and/or a predicted end time of slow wave sleep that is determined based on changes in cardiorespiratory parameters of the subject. Cardiorespiratory parameters in a subject typically begin to change before transitions between non-slow wave sleep and slow wave sleep. Predicting this time delay between the changes in the cardiorespiratory parameters and the onset and/or end of slow wave sleep facilitates better (e.g., more sensitive and/or more accurate) determination of slow wave sleep and/or non-slow wave sleep.

Abstract: The present disclosure pertains to a system for facilitating configuration modifications for a patient interface computer system based on a patient-specific risk alert model. In some embodiments, the system obtains (i) lifestyle information associated with a patient, (ii) disease information associated with the patient, and (iii) one or more physiological measurements of the patient. The system monitors the patient for one or more threshold levels of exacerbation based on a risk alert model, the lifestyle information, the disease information, and the one or more physiological measurements. The system causes a configuration of the patient interface computer system to be modified based on the monitoring for the one or more threshold levels of exacerbation.

Abstract: The present disclosure pertains to a system configured to determine one or more parameters based on cardiorespiratory information from a subject and determine sleep stage classifications based on a discriminative undirected probabilistic graphical model such as Conditional Random Fields using the determined parameters. The system is advantageous because sleep is a structured process in which parameters determined for individual epochs are not independent over time and the system determines the sleep stage classifications based on parameters determined for a current epoch, determined relationships between parameters, sleep stage classifications determined for previous epochs, and/or other information. The system does not assume that determined parameters are discriminative during an entire sleep stage, but maybe indicative of a sleep stage transition alone. In some embodiments, the system comprises one or more sensors, one or more physical computer processors, electronic storage, and a user interface.

Abstract: A method of generating an algorithm for determining a condition of a patient. The algorithm is generated based on medical characteristics that are shared by a group of data sources. The group of data sources is obtained by clustering a plurality of data sources into groups based on the medical characteristics associated with each data source.

Abstract: A system for detecting clinical deterioration in a patient having a health condition during one or more post-discharge patient follow-up periods is provided. The system includes a computer system that has one or more physical processors programmed with computer program instructions which, when executed cause the computer system to: determine a clinical deterioration risk detection model for each of the post-discharge patient follow-up periods, and determine a clinical deterioration risk detection value for the patient in the post-discharge patient follow-up period using the corresponding clinical deterioration risk detection model. The clinical deterioration risk detection model is a function of health information of the patient in the corresponding post-discharge patient follow-up period. The clinical deterioration risk detection value is configured to predict the likelihood of the clinical deterioration in the patient in the post-discharge patient follow-up period.

Abstract: The present disclosure pertains to a system configured to detect slow wave sleep and/or non-slow wave sleep in a subject during a sleep session based on a predicted onset time of slow wave sleep and/or a predicted end time of slow wave sleep that is determined based on changes in cardiorespiratory parameters of the subject. Cardiorespiratory parameters in a subject typically begin to change before transitions between non-slow wave sleep and slow wave sleep. Predicting this time delay between the changes in the cardiorespiratory parameters and the onset and/or end of slow wave sleep facilitates better (e.g., more sensitive and/or more accurate) determination of slow wave sleep and/or non-slow wave sleep.

Abstract: The present disclosure pertains to a system configured to determine one or more parameters based on cardiorespiratory information from a subject and determine sleep stage classifications based on a discriminative undirected probabilistic graphical model such as Conditional Random Fields using the determined parameters. The system is advantageous because sleep is a structured process in which parameters determined for individual epochs are not independent over time and the system determines the sleep stage classifications based on parameters determined for a current epoch, determined relationships between parameters, sleep stage classifications determined for previous epochs, and/or other information. The system does not assume that determined parameters are discriminative during an entire sleep stage, but maybe indicative of a sleep stage transition alone. In some embodiments, the system comprises one or more sensors, one or more physical computer processors, electronic storage, and a user interface.

Abstract: There is provided a method of processing measurements of acceleration to identify steps by a user, the method comprising obtaining measurements of acceleration from a device worn or carried by a user; analysing the measurements of acceleration to determine a value for a threshold that is to be used to identify steps by the user in measurements of acceleration; and using the determined value for the threshold to identify steps by the user in measurements of acceleration.