Abstract: A method for managing patient information in a system includes a cloud server, a healthcare facility, and affiliated facilities connected via a network. The method includes: receiving, from the healthcare facility, a medical image in response to examination order information sent by a first user from one affiliated facility; correlating the medical image to the examination order information and adding them to the patient information; correlating the medical image to disclosure propriety information indicating whether to allow the patient information to be disclosed to a second user; receiving a viewing request sent by the first or second user from one affiliated facility; determining whether to approve the viewing request based on the examination order information or the disclosure propriety information; and approving the viewing request and transmitting the patient information to the affiliated facility from which the viewing request has been sent.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for adjusting monitoring profiles of end devices. In one aspect, a method generates a health profile for the user; determines, based on the health profile of the user, a particular health risk for the user; determines, for the particular health risk, a first monitoring profile for the user that causes one or more health monitoring devices to monitor the user for a first set of health data; provides the first monitoring profile to the one or more health monitoring devices; receives epigenetic data describing epigenetic factors for the user; determines that a health change event for the particular health risk has occurred for the user; and in response, determines a second monitoring profile for the user for the particular health risk and provides the second monitoring profile to the one or more health monitoring devices.

Abstract: A method of managing an electronic health record and displaying a patient's medical state can include receiving a first input corresponding to the medical state, including a first plurality of positives that each correspond to a deviation from a healthy state. The method can also include grouping at least some of the first plurality of positives that collectively correlate to at least one of the medical conditions. The method can also include displaying with a display a statgraph having a plurality of objects including a homunculus plane, nodes projecting away from the plane, axes interconnecting the nodes, and a planar bundling object enveloping at least some of the nodes. The method can also include morphing, on the display controlled by the computing device, a first statgraph into a second statgraph.

Abstract: Automatically gathering and processing data relating to risk of patient falls. The data is analyzed to determine a level of risk each patient has for falling. Patient risk for falls is stratified and protocols are implemented to mitigate that risk. The system communicates instructions and alerts to caregivers to complete tasks or provide aid to patients. Updates to patient medical information and updates to best practices in fall risk management result in updates to patient risk stratifications. In turn, tasks and alerts are updated to reflect updates to patient risk stratifications.

Abstract: Brain modelling includes receiving time-coded bio-signal data associated with a user; receiving time-coded stimulus event data; projecting the time-coded bio-signal data into a lower dimensioned feature space; extracting features from the lower dimensioned feature space that correspond to time codes of the time-coded stimulus event data to identify a brain response; generating a training data set for the brain response using the features; training a brain model using the training set, the brain model unique to the user; generating a brain state prediction for the user output from the trained brain model, and automatically computing similarity metrics of the brain model as compared to other user data; and inputting the brain state prediction to a feedback model to determine a feedback stimulus for the user, wherein the feedback model is associated with a target brain state.

Abstract: A medical data governance system records, secures, and provides appropriate access to all patient data. By concentrating all available relevant medical data into a single source, and providing a subset of data to each receiving subsystem with the correct source and time reference, the medical data governance system becomes the true source of data and guarantees the data consistency through the use of block chain signatures.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for grouping brain parcellation data. One of the methods includes receiving brain parcellation data for a subject; receiving an indication from a user of a brain function category; forwarding data for display, the data comprising a set of functions within the brain function category; receiving a selection from the user of a brain function from the set of functions within the brain function category; determining a subset of the brain parcellation data for parcellations that have an overlap with the selected brain function where the overlap exceeds a threshold; and taking an action based on the determined subset of the brain parcellation data for parcellations that have an overlap with the selected brain function where the overlap exceeds a threshold.

Abstract: A plurality of events are extracted from a plurality of electronic health records associated with a first patient. The extracted plurality of events are analyzed to identify a plurality of stimulus events and a plurality of response events. An association between a first stimulus event and a first response event is determined. A stimulus-response (SR) variable is generated for the first patient based at least in part on the determined association, and the generated SR variable is integrated into one or more predictive cognitive models.

Abstract: Some embodiments of the present disclosure relate to systems, devices, and methods for making recommendations to users for, individually or together, goods, services, and information/media (e.g., audio, video, publications), as well as verifying the veracity of data/information, based upon an emotional impact of such goods/services and/or media/information on the user, other users, and/or other individuals, as well some embodiments directed to systems, devices, and methods for treating one or more brain functions, including neurological conditions and episodes associated therewith by determining (and optionally providing) media (e.g., audio, but in some embodiments, any of audio, textual information, and video), which may be referred to as optimal medical media (“OMM”).

Abstract: Techniques disclosed herein relate to monitoring changes in conditions of multiple individuals in areas. In some embodiments, a patient monitoring queue may be established (504) that includes a plurality of patients in an area (104, 304) such as a waiting room. The area may be capturable by vital sign acquisition camera(s) (276. 376, 476) mounted in or near the area. Updated vital sign(s) may be unobtrusively acquired (510) by the vital sign acquisition camera(s) from a given patient selected (506) from the patient monitoring queue. Based on the updated vital sign(s) and prior vital signs acquired previously from the given patient, deterioration of the given patient may be detected (512). Output may be provided (514) alerting medical personnel of the deterioration of the given patient.

Abstract: Systems and methods for using machine learning models to predict risks posed to patients having various respiratory conditions. According to certain aspects, an electronic device may generate a machine learning model using training data indicating various patient and medication data. The electronic device may access a set of real-world patient data and input the real-world patient data into the machine learning model. An output of the machine learning model may indicate a likelihood of the patients needing intervention care, and the electronic device may facilitate outreach efforts to certain patients in an attempt to decrease this likelihood.

Abstract: A system for computer-aided decision guidance includes and/or interfaces with a computing system. A method for computer-aided decision guidance includes: receiving a set of data; determining a set of parameters associated with the set of data; and triggering an output based on the set of parameters. Additionally or alternatively, the method can include analyzing the set of data and/or any other suitable processes.

Abstract: Techniques regarding autonomously updating a participation status of an entity with regards to a clinical trial are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise status component that can update a participation status of an entity regarding a clinical trial based on a trust disposition value. The trust disposition value can be determined using machine learning technology and can be indicative of an expected enrichment contribution associated with the participation status.

Abstract: Methods and systems to stratify risks for adverse health outcomes include aggregating data sets culled over a predetermined period of time; adjusting at least two variables within the aggregated data sets; and executing a regression analysis to identify an average causal effect between aggregated variables against at least one particular adverse health outcome.

Abstract: The disclosure provides a method for data instance processing. The method includes obtaining a set of data instances collected from a plurality of organizations. Each of the data instances includes at least one record formed in an organization that stores values of a plurality of attributes of the data instance. The method also includes dividing the set of data instances into groups, wherein data instances with conflicting values for the same attribute are divided into different groups. The method further includes subdividing data instances in each of the groups into clusters.

Abstract: A computerized system customizes patient care workflows for health service providers. The system may extract essential details from various health service records for an individual, transform those details into standardized information, and use the standardized information to generate health service workflows that are particular to the patient. The health service workflows may be customized to a particular health service venue, a particular health service provider role, or even a particular health service provider.

Abstract: Techniques regarding autonomously updating the status of a medical procedure are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a procedure component that can update a status of a medical procedure regarding an entity based on a trust disposition value derived using machine learning technology. The trust disposition value can be indicative of an expected efficacy associated with the medical procedure.

Abstract: Techniques regarding autonomously updating the status of a prescription are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a prescription component that can update a status of a prescription associated with an entity based on a trust disposition value. The trust disposition value can be determined using machine learning technology and can be indicative of an expected effectiveness of the prescription.

Abstract: Systems and methods for utilizing machine learning to generate a trans-diagnostic classifier that is operative to concurrently diagnose a plurality of different mental health disorders using a single trans-diagnostic questionnaire that includes a plurality of questions (e.g., 17 questions). Machine learning techniques are used to process labeled training data to build statistical models that include trans-diagnostic item-level questions as features to create a screen to classify groups of subjects as either healthy or as possibly having a mental health disorder. A subset of questions is selected from the multiple self-administered mental health questionnaires and used to autonomously screen subjects across multiple mental health disorders without physician involvement, optionally remotely and repeatedly, in a short amount of time.

Abstract: A method and system may provide an automated pharmacy processing system which automatically processes an electronic prescription by converting the electronic prescription into a pharmacy prescription record. In this way, an electronic prescription can be filled by a pharmacist without manual data entry. The systems, methods, and apparatuses may generate confirmation and verification requests based on electronic prescription data.