Abstract: Described herein are methods, devices and systems for predicting ventilator associated pneumonia (VAP). In an example, data of a patient including one or more features useful in predicting VAPs are accessed and the features are extracted. A model is activated based on a time parameter associated with mechanical ventilation or length of stay in ICU. The extracted features are evaluated automatically by the activated model to determine a probability that a VAP will occur. If it is determined, based on the evaluating, that a VAP has a probability of occurring that exceeds a threshold, an indication that the VAP is predicted to occur may be provided. Other examples are disclosed and claimed.

Abstract: Systems, apparatuses and methods provide for the management of electronic medical records (EMR) for clinical decision support (CDS) applications. For example, an apparatus (100) is configured to parse an EMR database (102), determine whether supplemental entry of current patient data is needed based on existing patient data in the EMR database, and transfer a notification that the supplemental entry of the current patient data is needed to a user interface (126) associated with a care provider.

Abstract: A security gateway for touch-free infectious disease screening includes a contactless sensor, a memory and a processor. The contactless sensor is configured to obtain health readings of a subject. The memory stores instructions. The processor executes the instructions. When executed by the processor, the instructions cause the security gateway to measure vital signs based on the health readings of the subject, and determine whether to allow access to a premise based on the vital signs.

Abstract: A method for automated detection of water accumulation in a ventilator, comprising: (i) a plurality of iterations of: calculating an airway pressure water accumulation index; calculating a flow water accumulation index; labeling, using a trained asynchrony detection algorithm, the single breath of the patient as synchronous or asynchronous, and ranking all of the input features based on an importance of each respective input feature to the synchronous or asynchronous determination; (ii) for the plurality of iterations; determining, a most frequent breath labeling, a most frequent most important input feature, and a most frequent most important input feature for each type of breath; and determining one or more of: (1) water accumulation and an action to remove the water accumulation; and (2) asynchrony due to a cause other than water accumulation, and an action to correct the asynchrony; and (iii) notifying a user of the determined action.

Abstract: A security gateway for touch-free infectious disease screening includes a contactless sensor, a memory and a processor. The contactless sensor is configured to obtain health readings of a subject. The memory stores instructions. The processor executes the instructions. When executed by the processor, the instructions cause the security gateway to measure vital signs based on the health readings of the subject, and determine whether to allow access to a premise based on the vital signs.

Abstract: Methods and systems for protecting digital client data transmitted to a computing cloud (200) for data processing ensure privacy of the client data by transmitting only scrambled client data to the computing cloud and never storing descrambled client data in the computing cloud. To the extent that descrambling is necessary for processing the client data with the computing cloud, descrambling is embedded in the processing so that descrambled client data is never output or stored by the computing cloud.

Abstract: An embodiment provides objective measures or indications to facilitate evaluation of readiness and successful progress or completion of spontaneous breathing and awakening trials (SATs/SABs) in intensive care settings. One example includes obtaining a patient state indicating that the patient is associated with a SAT or SBT protocol. Imagery of the patient for the protocol is obtained and evaluated to classify the imagery as indicative of an observation item associated with the protocol. An indication based on the evaluating is provided, for example to a clinical decision support system.

Abstract: A CDS system including a controller extracting EHRs of an IHG and selecting SMs to form a target patient database, capturing a time interval between each subsequent record for each of the measurements defined by the SMs, computing hospital level features including statistics on the distribution of the captured time intervals for each of the measurements, clustering the plurality of hospitals of the IHG in accordance with the computed hospital level features, determining whether an other hospital is within a threshold distance to a closest one of the plurality of clusters; placing the other hospital in the closest cluster when it is determined that the other hospital is within the threshold distance, and placing the other hospital in a new cluster when it is determined that the other hospital is not within the threshold distance.

Abstract: Described herein are methods, devices and systems for predicting ventilator associated pneumonia (VAP). In an example, data of a patient including one or more features useful in predicting VAPs are accessed and the features are extracted. A model is activated based on a time parameter associated with mechanical ventilation or length of stay in ICU. The extracted features are evaluated automatically by the activated model to determine a probability that a VAP will occur. If it is determined, based on the evaluating, that a VAP has a probability of occurring that exceeds a threshold, an indication that the VAP is predicted to occur may be provided. Other examples are disclosed and claimed.

Abstract: A method and a system of estimating a pose of a subject is disclosed. The method includes receiving a video stream from an imaging device in real-time; applying a trained computational model to extract feature maps from images received from the video stream; determining initial estimates of heatmaps and part affinity fields (pafs) from the extracted feature maps; refining the initial estimates of the heatmaps and pafs to output refined heatmaps and pafs; refining the heatmaps and pafs upon completing the refining of the initial estimates using a self-attention module; detecting keypoints on the heatmaps; and performing graph matching on the pafs to group the keypoints to different subjects.

Abstract: Described herein are methods, devices and systems for predicting ventilator associated events (VAEs). In an example, data of a patient including one or more features useful in predicting VAEs is accessed and the features are extracted. The extracted features are evaluated automatically to determine a probability that a VAE will occur. If it is determined, based on the evaluating, that a VAE has a probability of occurring that exceeds a threshold, an indication that the VAE is predicted to occur is provided. Other examples are disclosed and claimed.

Abstract: A system and method receiving patient information entered via a user interface, processing the patient information to generate an initial worklist based on the patient information and a patient guideline, displaying the initial worklist, receiving a user input based on the initial worklist, processing the user input to generate an updated worklist based on the user input and displaying the updated worklist.

Abstract: Example embodiments are directed to a video image display system that includes a motion estimation circuit (112), a front-end motion compensation circuit (110), and a video signal conversion circuit (134, 136). The motion estimation circuit generates motion vectors as a function of an incoming video signal, the front-end motion compensation circuit receives and processes the incoming video signal as a function of the motion vectors for general video display, and the video signal conversion circuit receives the processed video signal from the front-end motion compensation circuit and generates a display signal for a specific video display as a function of both the processed video signal and the motion vectors. In one implementation, a scaler (120) is implemented to scale the video signal for a particular display type, with components of the motion compensation being implemented for use with the scaler.

Abstract: For use in a video processing system that is capable of processing a video stream using a chain of video processing algorithms, there is disclosed a system and method for optimally configuring control parameter settings of each video processing algorithm within the chain of video processing algorithms in order to provide a high quality video image. The video processing system of the present invention comprises a chain of video processing algorithms, an optimization unit, and an objective quality metric unit. An output video stream from the chain of video processing units is fed back to the objective quality metric unit. The objective quality metric unit calculates a fitness value and provides the fitness value to the optimization unit. The optimization unit uses the fitness value to configure the control parameter settings for the video processing algorithms. In one advantageous embodiment of the present invention, the optimization unit uses a genetic algorithm in the optimization process.

Abstract: There is disclosed an apparatus for controlling the processing load of a scalable video decoder that decodes an incoming scalable video bit stream and generates a baseband video signal. The apparatus comprises: 1) an analyzer circuit for measuring at least one characteristic of the incoming scalable video bit stream and generating at least one video parameter associated with the at least one characteristic; and 2) a processor load controller for receiving the at least one video parameter and, in response thereto, controlling a level of decoding of the incoming scalable video bit stream performed by the scalable video decoder.

Abstract: There is disclosed an improved system and method for providing a scalable objective metric for automatically evaluating the video quality of a video image. The system comprises an objective metric controller that is capable of receiving a plurality of objective metric figures of merit from a plurality of objective metric model units. Some of the objective metric model units are independent and some are interdependent. The system determines a scalable objective metric from the plurality of objective metric figures of merit. The scalable objective metric represents the best correlation of objective metric measurements of the video image with subjective measurements of the video image. The system is capable of continually determining a new value of the scalable objective metric as the plurality of objective metric model units receive new video images.

Abstract: There is disclosed an improved system and method for providing a scalable dynamic objective metric for automatically evaluating the video quality of a video image. The system comprises an objective metric controller that is capable of receiving a plurality of objective metric figures of merit from a plurality of objective metric model units. The system determines a scalable dynamic objective metric from a weighted average of the plurality of objective metric figures of merit. The scalable dynamic objective metric represents the best correlation of objective metric measurements of the video image with subjective measurements of the video image. The weight value of individual objective metric figures of merit may be increased or decreased depending upon the type of video image being evaluated. Individual objective metric figures of merit may be added to the system or deleted from the system.

Abstract: For use in a video processing system that is capable of processing a video stream using a chain of video processing algorithms, there is disclosed a system and method for optimally configuring control parameter settings of each video processing algorithm within the chain of video processing algorithms in order to provide a high quality video image. The video processing system of the present invention comprises a chain of video processing algorithms, an optimization unit, and an objective quality metric unit. An output video stream from the chain of video processing units is fed back to the objective quality metric unit. The objective quality metric unit calculates a fitness value and provides the fitness value to the optimization unit. The optimization unit uses the fitness value to configure the control parameter settings for the video processing algorithms. In one advantageous embodiment of the present invention, the optimization unit uses a genetic algorithm in the optimization process.

Abstract: There is disclosed an improved system and method for providing a scalable objective metric for automatically evaluating the video quality of a video image. The system comprises an objective metric controller that is capable of receiving a plurality of objective metric figures of merit from a plurality of objective metric model units. Some of the objective metric model units are independent and some are interdependent. The system determines a scalable objective metric from the plurality of objective metric figures of merit. The scalable objective metric represents the best correlation of objective metric measurements of the video image with subjective measurements of the video image. The system is capable of continually determining a new value of the scalable objective metric as the plurality of objective metric model units receive new video images.

Abstract: There is disclosed an improved system and method for providing a scalable dynamic objective metric for automatically evaluating the video quality of a video image. The system comprises an objective metric controller that is capable of receiving a plurality of objective metric figures of merit from a plurality of objective metric model units. The system determines a scalable dynamic objective metric from a weighted average of the plurality of objective metric figures of merit. The scalable dynamic objective metric represents the best correlation of objective metric measurements of the video image with subjective measurements of the video image. The weight value of individual objective metric figures of merit may be increased or decreased depending upon the type of video image being evaluated. Individual objective metric figures of merit may be added to the system or deleted from the system.