Abstract: A system includes: a distributed computing subsystem to execute an adjustable number of instances of a request handling process; and a scaling control subsystem connected with the distributed computing subsystem to: allocate received requests among the instances of the request handling process; receive respective self-perceived load indicators from each of the instances of the request handling process; generate, based on the self-perceived load indicators, a total load indicator of the distributed computing subsystem; and compare the total load indicator to a threshold to select an adjustment action; and instruct the distributed computing subsystem to adjust the number of instances of the request handling process, according to the selected adjustment action.

Abstract: Techniques are described for identifying complex patients and forecasting patient outcomes based on a variety of factors including medical, socio-economic, mental and behavioral. According to an embodiment, a method can include employing one or more machine learning models to identify complex patients and predict patient outcomes like length of stay, potential discharge trajectories with likelihoods, discharge destinations, readmission likelihood and safety. These models are applied to respective patients that are currently admitted to a hospital and expected to be placed after discharge from the hospital, wherein the one or more discharge forecasting machine learning models predict the discharge destinations based on clinical data points and non-clinical data points collected for the respective patients.

Abstract: Techniques are described for identifying complex patients and forecasting patient outcomes based on a variety of factors including medical, socio-economic, mental and behavioral. According to an embodiment, a method can include employing one or more machine learning models to identify complex patients and predict patient outcomes like length of stay, potential discharge trajectories with likelihoods, discharge destinations, readmission likelihood and safety. These models are applied to respective patients that are currently admitted to a hospital and expected to be placed after discharge from the hospital, wherein the one or more discharge forecasting machine learning models predict the discharge destinations based on clinical data points and non-clinical data points collected for the respective patients.

Abstract: In one example, an electromechanical device includes a removable component comprising an electronic identification tag. The apparatus also includes a plurality of tag readers disposed at a plurality of sectors in the apparatus to detect the electronic identification tag and signal strengths associated with the detected electronic identification tag, and send the signal strengths to a tracking system.

Abstract: Movement of a mobile computing device can be guided so that a current focal distance of a camera of the mobile computing device is within a focal distance range within which other mobile computing devices of a same model type captured images at a same location and in which the visual code was successfully detected. Movement of the mobile computing device can be guided so that a distance between prominent points within an image captured by the camera is within prominent points distance range calculated from a distance between the prominent points within each image captured by the other mobile computing devices. An exposure of the camera can be adjusted to be within a range of exposures at which the other mobile computing devices captured the images.

Abstract: Movement of a mobile computing device can be guided so that a current focal distance of a camera of the mobile computing device is within a focal distance range within which other mobile computing devices of a same model type captured images at a same location and in which the visual code was successfully detected. Movement of the mobile computing device can be guided so that a distance between prominent points within an image captured by the camera is within prominent points distance range calculated from a distance between the prominent points within each image captured by the other mobile computing devices. An exposure of the camera can be adjusted to be within a range of exposures at which the other mobile computing devices captured the images.

Abstract: In one example, an electromechanical device includes a removable component comprising an electronic identification tag. The apparatus also includes a plurality of tag readers disposed at a plurality of sectors in the apparatus to detect the electronic identification tag and signal strengths associated with the detected electronic identification tag, and send the signal strengths to a tracking system.