Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive prioritization. Certain embodiments utilize systems, methods, and computer program products that perform predictive prioritization using a combination of supervised machine learning models and unsupervised machine learning models that are in turn used to generate target features for a resultant prioritization machine learning model.

Abstract: Systems and methods are disclosed for scheduling appointments based on changing health conditions of users. The method includes inputting the initial health dataset of a user to a machine learning model configured to identify other users with similar health profiles. A frequency of monitoring, a frequency of appointments, a duration between the appointments, or a type and length of the appointments is determined to generate a schedule of appointments. A subsequent health dataset of the user is received, and health scores, rule scores, or medication scores for the user are determined based on the subsequent health dataset. A plurality of risk scores for the user is evaluated based on the health scores, rule scores, or medication scores. A recent risk score is determined based on a change in the plurality of risk scores. The schedule of appointments is adjusted based on the recent risk score and the user is notified.

Abstract: Methods, systems, and non-transitory computer readable mediums are disclosed for recommending medically-relevant articles. One method comprises receiving health data associated with a patient, storing the health data in a data structure as historical patient data, identifying new appointment scheduling data associated with the patient, determining an appointment subject associated with the new appointment scheduling data, determining a correlation between the new appointment scheduling data and the historical patient data, searching one or more databases for one or more relevant articles, retrieving the one or more relevant articles, generating a recommendation for the one or more relevant articles, and presenting the recommendation for the one or more relevant articles to a user through at least one of a news feed, a calendar, or an electronic medical record (EMR) associated with the patient.

Abstract: Apparatuses, systems, and methods for more accurate remote monitoring of a user's body to stabilize the user during fall events and to thereby prevent the user from falling. In some embodiments, a wearable device comprising a power source, one or more sensors configured to monitor a user's COG (COG), at least one plurality of electrodes, a communications interface and a control device is provided. The wearable device is configured to apply electrical pulses according to defined electrical pulse stimulation protocols via the electrodes to target muscle groups of the user's body, causing those target muscle groups to contract and thereby stabilize the user's body during a fall event.

Abstract: An example method includes obtaining, by processing circuitry, an image depicting a face of a person wearing eyewear and a reflection pattern generated by projecting light onto a lens of the eyewear and determining, by the processing circuitry, an optical parameter of the lens based on the image. The method further includes receiving, by the processing circuitry and from an electronic database, a personal characteristic data relating to a previously identified person and comparing, by the processing circuitry, the optical parameter of the lens to the personal characteristic data. The method further includes determining, by the processing circuitry and based on the comparison, a score indicative of a match between the person wearing eyewear and the previously identified person.

Abstract: An example method includes obtaining, by a computing system, first audio data representing one or more initial utterances during an interactive voice session with an interactive voice system; generating, by the computing system, based on the first audio data, a prediction regarding whether a subsequent utterance of a user in the interactive voice session will contain sensitive information, wherein the subsequent utterance follows the one or more initial utterances in time; obtaining, by the computing system, second audio data representing the subsequent utterance; determining, by the computing system, based on the prediction and based on a risk profile of the interactive voice system, whether to transmit the second audio data to the interactive voice system; and based on the determination to transmit the second audio data to the interactive voice system, transmitting the second audio data to the interactive voice system.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a model deficiency data object for a tensor-based graph processing machine learning model. Certain embodiments of the present invention utilize systems, methods, and computer program products that generate a model deficiency data object for a tensor-based graph processing machine learning model using holistic graph links generated by utilizing a graph representation machine learning model.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: obtaining an audio input, the audio input representing sound emanating from a key press based user interface; generating a context pattern based on the audio input; performing classification of the context pattern to classify the context pattern as belonging to a signature pattern classification, wherein the signature pattern classification specifies a user activity; and providing an output based on the performing classification.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing splinting activity detection. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform splinting activity detection using at least one of splinting activity detection machine learning models, observed inspiration-expiration waveform pattern, and expected inspiration-expiration waveform patterns.

Abstract: There is a need to automatically provide one or more electronic documents to the user. In one example, embodiments comprise, generating a facility visit data object for a user describing one or more facility attributes for one or more facilities associated with a potential visit from the user. One or more electronic documents may be determined to be retrieved based at least in part on the facility visit data object. One or more user interaction electronic document data objects may be generated to enable interaction between the user and the one or more electronic document data objects. One or more modified user interaction electronic document data objects may be received and may be provided to one or more facility computing entities in response to one or more trigger events.

Abstract: Embodiments of the invention provide apparatuses, systems, and methods for more accurate remote monitoring of a user's body. In some embodiments, a system for monitoring a user's body comprises a wearable device, a video sensor attached at a collar portion of the wearable device, a plurality of audio sensors spaced and attached at a body portion of a wearable device and a controller configured to determine a Jugular Venous Pressure (JVP) of the user, and determine audio characteristics of an output of the plurality of audio sensors to generate an audio heat map corresponding to at least one internal organ of the user.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for multi-node system monitoring using one or more distributed ledgers, wherein a ledger entry request received from a miner node computing entity is validated based at least in part on executing a hierarchical validation workflow that comprises an ordered sequence of L validation iterations, wherein each of one or more ith non-initial validation iterations is performed based at least in part on an association of an (i?1)th validation iteration with a non-affirmative validation determination.

Abstract: A method comprises obtaining, by a computing system, first audio data representing one or more initial utterances during an interactive voice session with an interactive voice system; generating, by the computing system, based on the first audio data, a prediction regarding whether a subsequent utterance of a user during the interactive voice session will contain sensitive information, the subsequent utterance following the one or more initial utterances in time; obtaining, by the computing system, second audio data representing the subsequent utterance; determining, by the computing system, based on the prediction, whether to transmit the second audio data; and based on a determination not to transmit the second audio data: replacing, by the computing system, the second audio data with third audio data that is based on a voice of the user; and transmitting, by the computing system, the third audio data.

Abstract: Apparatuses, systems, and methods for more accurate remote monitoring of a user's body to stabilize the user during fall events and to thereby prevent the user from falling. In some embodiments, a wearable device comprising a power source, one or more sensors configured to monitor a user's COG (COG), at least one plurality of electrodes, a communications interface and a control device is provided. The wearable device is configured to apply electrical pulses according to defined electrical pulse stimulation protocols via the electrodes to target muscle groups of the user's body, causing those target muscle groups to contract and thereby stabilize the user's body during a fall event.

Abstract: Certain embodiments provide systems, methods, and computer program products that administer pain management treatments remotely, according to a dynamic prescription. In some embodiments, the dynamic prescription provides for changes in a dosage of a medication or electrical stimulation, based for instance upon changes in the patient's pain tolerance level, biological indicators, or behavioral characteristics. In some embodiments, if it is determined that a dosage change is needed, the dosage change can be compared against the dynamic prescription to determine whether the dosage change is permitted within the scope of the dynamic prescription. In some embodiments, if the dosage change is determined to be outside what is permitted by the scope of the dynamic prescription, the updated dosage information can be provided to the patient's physician for authorization of the updated dosage.

Abstract: Various embodiments of the present invention disclose techniques for traversal path optimization given a traversal network comprising a group of nodes comprising a plurality of navigation orchestration nodes and using a traversal path optimization machine learning model. In some embodiments, a path feature set is determined for each candidate traversal path of a plurality of candidate traversal paths. A traversal path optimization machine learning model is configured to generate path scores for each candidate traversal path based at least in part on the path feature set.

Abstract: Various embodiments of the present disclosure provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations using a hierarchical risk prediction machine learning framework that comprises an initially-deployed risk prediction machine learning model, a dynamically-deployed risk prediction machine learning model, and a risk aggregation machine learning model. In some embodiments, the dynamically-deployed risk prediction machine learning model is deployed when a dynamic deployment training entry count of one or more dynamic deployment training entries satisfies a dynamic deployment training entry count threshold.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations using a hierarchical intervention recommendation machine learning framework. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations using at least one of the techniques using real-time sensory timeseries data object, techniques using global baseline sensory feature data object, techniques using intermediate intervention operations, techniques using real-time risk scores, techniques using intermediate risk scores, and/or the like.

Abstract: A computing system includes a storage device and processing circuitry. The processing circuitry is configured to obtain an image frame that comprises a plurality of pixels that form a pixel array. Additionally, the processing circuitry is configured to determine that a region of the image frame belongs to a trigger content type. Based on determining that the region of the image frame belongs to the trigger content type, the processing circuitry is configured to modify the region of the image frame to adjust a luminance of pixels of the region of the image frame based on part on an ambient light level in a viewing area of the user; and output, for display by a display device in the viewing area of the user, a version of the image frame that contains the modified region.

Abstract: A system and method for disabling a function of a feature of a vehicle based on a validation request to a centralized validation authority, prior to a use of the feature includes determining that a condition is met as part of a startup sequence of the vehicle, transmitting the validation request to the centralized validation authority, in response to determining the condition is met, wherein the validation request is part of the startup sequence of the vehicle, receiving a validation decision from the centralized validation authority, and disabling the feature of the vehicle as a function of the receiving the validation decision.