A predictive patient health machine learning model is trained based on baseline health data configured as directed graphs. Patient-healthcare system encounter data formed at least in part by electronic medical records (EMRs) is gathered. The patient-healthcare system encounter data is configured as directed graphs to generate graphed health data and the predictive patient health machine learning model is trained on that graphed health data.

A computer-implemented method for providing anonymized patient datasets, comprises: analyzing statistical population data to ascertain obfuscation parameters; and anonymizing patient datasets including quasi-identifiers as attributes by obfuscating the quasi-identifiers of the patient datasets based on the obfuscation parameters to generate the anonymized patient datasets. A system includes at least one processor and a memory, and is configured to provide the anonymized patient datasets.

A computer-implemented method for providing anonymized patient datasets, comprises: analyzing statistical population data to ascertain obfuscation parameters; and anonymizing patient datasets including quasi-identifiers as attributes by obfuscating the quasi-identifiers of the patient datasets based on the obfuscation parameters to generate the anonymized patient datasets. A system includes at least one processor and a memory, and is configured to provide the anonymized patient datasets.

Systems and methods for providing algorithmic equipment and/or accessory recommendations are disclosed herein. In one embodiment, a method providing an equipment or accessory recommendation to an athlete includes: monitoring a first amplitude of a first muscle of the athlete by a first wearable muscle response sensor carried by the athlete; monitoring a second amplitude of a second muscle of the athlete by a second wearable muscle response sensor carried by the athlete; determining a difference between the first amplitude and the second amplitude; comparing the difference to a predetermined amplitude threshold; and based on the comparing, providing an equipment or accessory recommendation to the athlete.

A mobility augmentation system assists a user's movement by determining a corresponding electrical stimulation for the movement. A wearable stimulation array includes sensors, electrodes, an electrode multiplexer, and a controller that executes the mobility augmentation system. The sensors measure movement data, and the mobility augmentation system applies a movement model to the measured movement data. The model can determine different electrical actuation instructions depending on the movement stimulated. For example, to stimulate a knee flexion, the movement model output enables a first set of the electrodes to operate as cathodes and a second set of electrodes to operate as anodes. To stimulate a knee extension, the first set of electrodes can be enabled to operate as anodes and a third set of electrodes as cathodes. The user can provide feedback of the applied stimulation, which the system can use to retrain the model and optimize the stimulation to the user.

A virtual care system can include a location module configured to receive location information associated with a device from the device, and a graphical user interface (GUI) module configured to generate a medical provider user interface accessible via the device. The medical provider user interface can be contextually generated based on the location information and includes interface characteristics associated with the location information.

Systems and methods for remotely monitoring hospital patients are provided. Medical devices may capture biometric data associated with a patient positioned in an ICU environment, and a remote display device positioned external to the ICU environment may display an indication of the health of the patient based on the captured biometric data. The indication of the health of the patient may include a graph mapping normalized health statuses associated with the patient over time. Each normalized health status may be based on normalizing a particular type of biometric data based on a medical protocol, so each data point of the graph includes an indication of a normalized health status associated with the patient at a given time. The indication of the health of the patient may include an animated three-dimensional that is dynamically updated to reflect biometric data associated with the patient captured at a time selected by a user.

Embodiments of a risk assessment and intervention platform architecture are disclosed, where the risk assessment and intervention platform can predict and reduce negative medical outcomes. Embodiments of the risk assessment and intervention platform may include components with controls or interface elements to receive instructions from clinicians to set parameters for interventions and then to enroll, track, and monitor patient activity through the medical treatment plans. Embodiments of a scoring engine are configured based on a specific medical treatment plan and intervention protocols to initiate intervention as needed.

An eye test administered to a remote subject over a computer network, where sensors provide feedback through the computer network so that light source position, light source brightness and/or a display configuration and/or parameter setting related to a visual display that is used to implement the remote eye test.

An eye test administered to a remote subject over a computer network, where sensors provide feedback through the computer network so that light source position, light source brightness and/or a display configuration and/or parameter setting related to a visual display that is used to implement the remote eye test.