Abstract: A smart scheduling and information gathering system to promote sleep and mental health in unprecedented emergency situations. The system includes a remote server used for gathering contextual event data from external data sources, capturing physiological data from an environmental sensor, generating a sleep availability rating by analyzing a sleep activity log, generating a scheduling availability rating by analyzing a current-date-and-time in relation to a user calendar managed by the remote server, and generating a behavioral recommendation and a filtering protocol by analyzing the contextual event data, the physiological data, the sleep availability rating, and the scheduling availability rating. The system further includes a user computing device used for outputting the behavioral recommendation, comparing incoming data to the filtering protocol in order to classify the incoming data as authorized data or unauthorized data, and outputting the authorized data.

Abstract: A mechanism for identifying orthosomnia, being the preoccupation with sensor-derived sleep measures or automated sleep analysis processes, within an individual. Interactions between the individual and a software application performing a sleep analysis process are monitored and used to generate a predictive indicator that indicates a likelihood that the individual has orthosomnia. This predictive indicator may be used to control the software application, and in particular, to control the display of information provided by the software application.

Abstract: A melatonin optimization system detects users' hormone sensitivity through sleep architecture monitoring and recommends a dose of melatonin personalized to each user in relation to the user's behaviors, needs and health conditions. Determining an optimized melatonin dose requires accurate prediction of non-intervention sleep onset latency for the upcoming sleep period so that the dose can be based on the difference between the user's desired sleep onset latency and the predicted non-intervention sleep onset latency. The system can use either a general population-based sleep onset latency prediction model or a machine learning model trained to be personalized for each user.

Abstract: An apparatus and method involve not just comparing a patient's recovery but improving predications by grouping the patient into a phenotype to trend the patient's rate of progress compared to existing phenotype clusters to predict, based on a comparison of rate of progress of decline, and assess in order to benefit in the improvement of allocation and timing of future resource needs, including the economic impact.