Abstract: A learning device includes: a psychological state data preprocessing unit that calculates a duration of a psychological state from psychological state sequence data, and generates preprocessed psychological state sequence data including the psychological state and the duration; and a learning unit that learns a psychological state sequence prediction model, using input sequence data including behavior sequence data and the preprocessed psychological state sequence data, and correct sequence data that is preprocessed psychological state sequence data at a time later than the input sequence data.

Abstract: A system comprising at least one processor and a memory is disclosed. The memory includes instructions that, when executed by the at least one processor, cause the at least one processor to receive information pertaining to an environment of a therapeutic procedural pack, wherein the therapeutic procedural pack comprises a therapeutically effective item for a therapeutic procedure; store the information in a database; using machine learning, process the information to generate processed information; using the processed information, determine an automated control response; and using the automated control response, generate instructions that, when executed by a machine, cause the machine to modify a step in a therapeutic procedural pack manufacturing process.

Abstract: A central computing entity receives an encrypted request for performance of a back-end function, the encrypted request associated with a provider and a corresponding practice management system; generates a trigger indication that comprises patient identifying information based on the encrypted request; processes the trigger indication using a program code module corresponding to the back-end function and operating on the central computing entity to generate a response; converts the response into a notification in a format corresponding to the practice management system; and encrypts and provides the notification such that a user computing entity receives the notification. The user computing entity may be configured to provide at least a part of the notification or a graphical representation thereof via a user interface of the user computing entity.

Abstract: The present invention describes a device, a system, and a process for storing clinical-surgical data from clinical-surgical environment. Specifically, the present invention comprises an integrating means provided with a processor capable for receiving clinical-surgical data from a plurality of signal sources and for generating integrated and resized data to promote the storage of such data, providing a high degree of security in order to prevent frauds or damage to data/signals generated in the clinical-surgical event. The present invention pertains to the fields of health, medicine, and information technology.

Abstract: A system and methods for presenting an ailment from a modified nourishment scheme, the system comprising a computing device configured to identify a nutrition requirement for a user, receive, from a monitoring device, a monitoring element, generate a modified nutrition requirement as a function of the monitoring element, and identify an ailment that fulfills the modified nutrition requirement wherein identifying the ailment comprises determining a nourishment value corresponding to the modified nutrition requirement, creating a distance metric from the nourishment value to each ailment of a plurality of ailments, and selecting at least an ailment as a function of a minimal distance metric calculation.

Abstract: Methods of performing experimental treatments on a cohort of subjects are provided. A predictive model can be utilized to predict progression of a medical disorder or relevant imaging biomarker. The predicted medical disorder progression can be utilized as a control to determine whether an experimental treatment has an effect on the progression of the medical disorder. In some instances, the enrollment of subjects within a control group for clinical experiment is eliminated or reduced.

Abstract: Apparatus and associated methods relate to a Self-Validating System (SVS) which includes an Electronic Self-Validating Module (ESVM) that may be employed within a medical device, operable to receive and to transmit wireless transmissions to and from a personal electronic mobile device, the mobile device executing a Self-Validating Medical Device Remote Control (SVMDRC) application, also known as the mobile app, operable to control the ESVM, the mobile device and the ESVM further executing a pre-determined sequence of instructions at a pre-determined frequency, operable to perform a set of safety (validation) checks on the mobile device, and the ESVM. In an illustrative example, changes (updates) to the mobile device's operating system, the app, or the hardware on the mobile device may be detected by the mobile device, and may advantageously re-validate the mobile device automatically.

Abstract: A server computer processes infusion pump data for presentation on an operator interface. The computer includes a network circuit configured to provide communications over a network and a processing circuit. The processing circuit receives infusion pump history data from a plurality of infusion pumps, including an indication that a user inputted to an infusion pump a parameter which was outside of a prestored limit, an indication that a user was notified that the parameter was outside the prestored limit, an indication that the infusion pump parameter was returned to within the prestored limit, and an indication that the user started infusion on the pump at the parameter within the prestored limit. Display data indicating reprogram and override events is generated for display on an operator interface.

Abstract: A method for predicting a depressive state using a wearable device. Each data type of biological data for several days from plurality of subjects is converted into data in predetermined time unit (S201). Next, quantiles of the distribution of obtained sample data are determined for each subject and each data type (S202). The standard deviation of distribution of the obtained sample data is calculated for each subject and each data type (S203). The Pearson correlation coefficient is calculated for each combination of data types for each subject (S204). Next, a prediction model for the classification problem of whether subject is in a depressive state is trained by machine leaning (S206), wherein quantiles, standard deviations, and Pearson correlation coefficients extracted from the biological data are features used for an input vector, and an evaluation of existence or non-existence of a depressive state by an expert is a label used as teacher data.

Abstract: Various embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by using an agent machine learning model to determine an optimal clinical intervention based at least in part on the current clinical state and an inferred reinforcement learning policy that is determined based at least in part on a familiarity-adjusted reward function, where the familiarity-adjusted reward function is generated by an environment machine learning framework based at least in part on one or more next state predictions for one or more pruned action-state combinations based at least in part on a historical clinical outcome database, and the one or more pruned action-state combinations are determined based at least in part on one or more pruned clinical actions that are selected from a plurality of candidate clinical actions based at least in part on one or more action pruning criteria.

Abstract: Systems, methods, and user interfaces provide integrated coordination of care. Care team coordination and collaboration is promoted by bringing together each of the necessary elements of a patient's plan into a single point of access. Current workflow silos that exist in the care planning space are eliminated. This allows seamless support for the many different care planning regulations across care settings and supports and involves the entire care team including the patient and personal care team.

Abstract: Methods, systems, and devices for patient monitoring are described. The method may include receiving data related to a physiological parameter of a patient. The method may further include displaying information related to the physiological parameter of the patient in order to indicate how the patient is trending. In some cases, the method may include adjusting the received data and determining a subset of the adjusted values to display at a computing device. In some other cases, the method may include displaying a health indicator based on the health record of the patient on the computing device. The position of the health indicator may be based on a medical relationship between the data indicated by the health indicator and the physiological parameter.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving user input, the user input indicating a user command to display a waveform strip screen, in response to the user input, processing patient-specific data to provide waveform data, and displaying the waveform strip screen on the mobile device, the waveform strip screen displaying one or more waveform strips, each waveform strip of the one or more waveform strips being based on the waveform data and graphically depicting a physical waveform strip.

Abstract: Computer-implemented methods, computer-readable storage media, and computer systems for associating received medical imaging data with stored medical imaging data. Medical imaging data associated with a patient is received from a client device. The medical imaging data includes an image object, one or more data objects related to the image object, and metadata associated with the patient encoded in Digital Imaging and Communication in Medicine (DICOM) format. The metadata includes personal information about the patient, and time and location at which the DICOM image file was captured. The received metadata is compared with existing metadata associated with multiple existing patients to identify a subset of the existing patients who may be a potential match to the patient. Selection of an existing patient from among the subset is requested. When received, the medical imaging data is associated with the existing patient.

Abstract: A medical device for assessing clinical patient deterioration in an in-patient hospital environment is provided. The medical device includes a processor and sensors that couple externally to a skin of the patient to acquire electrocardiogram (ECG) and other physiologic signals. The processor is configured to receive a medical history of the patient; generate physiologic data, including ECG data, over a period of time based on one or more physiologic signals; and execute a risk assessment process associated with a clinical condition of the patient. The risk assessment process analyzes the physiologic data and the medical history of the patient to generate a risk estimate of deterioration of the patient's clinical condition.

Abstract: Disclosed are a device, a computer-readable medium, and techniques that provide an onboarding process and an adaptivity process for a drug delivery device. A processor executing an onboarding process determines whether a history of delivered insulin to a user meets certain sufficiency requirements. The onboarding process enables a processor to cause the drug delivery device to administer doses of insulin to a user according to an initial total daily insulin dose calculation that is determined based on the sufficiency of the insulin delivery history. The initial total daily insulin may be adapted according to the adaptivity process as new insulin delivery is collected. The insulin delivery history, when sufficient, may be used to set total daily insulin dosages that enable automated insulin delivery upon replacement of a drug delivery device. The adaptivity process may be implemented to modify an initial insulin delivery doses to provide adapted insulin delivery doses.

Abstract: The present disclosure is generally directed to an apparatus for outputting at least a recipe to a user is described. The apparatus may include at least a processor, and a memory communicatively connected to the processor. The memory may contain instructions for configuring the at least a processor to receive user data from a user. The processor may be configured to classify the user data to a one or more phenotypic clusters and assign the user one or more cohort labels as a function of the one or more phenotypic clusters. Further, the processor may be configured to generate alimentary data as a function of the one or more phenotypic cluster and the one or more cohort labels. Moreover, the processor may be configured to output an alimentary program to the user.

Abstract: Some embodiments include a system and computer-implemented method for aggregating and tracking medical delivery to a patient including a non-transitory computer-readable medium in data communication with at least one processor, where the non-transitory computer-readable medium includes software instructions for a medical services tracking system and method. Upon execution of the software instructions, information from a patient database or server can be received and displayed a medical record dashboard. A user can view and edit access to the information, and a user selectable link can display medical record information. The system and method enable auto-population of medical data entry fields based at least one part on at least one claim made or billing signed off by a physician for at least one medical service or procedure previously provided to or performed on at least one patient.

Abstract: A surgical hub may obtain a hub connectivity mode based on a hub connectivity control parameter. For example, the hub connectivity mode may be selected from multiple connectivity modes that may be preconfigured, dynamically updated, semi-dynamically updated, periodically updated, or preset. The hub connectivity modes may control inter-device connectivity within a network associated with a hospital, and/or communication with an external network associated with a different hospital. The surgical hub may determine whether to provide instructional information to at least one smart surgical instrument based on the hub connectivity mode. On a condition that the hub connectivity mode does not support provisioning instructional information to surgical devices, provisioning instructional information to surgical devices may be disabled.

Abstract: A virtual health-care electronic commerce community includes a network to communicate information relating to the community; one or more patients coupled to the network; one or more treating professionals coupled to the network; and a server coupled to the network, the server storing data for each patient and performing patient data visualization in response to a user request.