Abstract: A method and system for predicting the next location for a patient in a healthcare facility, including: defining a location-procedure co-occurrence matrix for the healthcare facility, wherein the location-procedure co-occurrence matrix define the probability that a procedure will be performed in a specific location; defining a procedure transition matrix, wherein the procedure transition matrix defines the probability of moving from a first procedure to a second procedure; defining a patient input vector based upon the patient condition and procedures performed on the patient; calculating an output vector based upon the patient input vector and the procedure transition matrix; producing a procedure vector by setting all values in the output vector to zero except for the N highest values in the output vector, where N is an integer; calculating a location prediction vector based upon the procedure vector and the location-procedure co-occurrence matrix; and transmitting information regarding the M most likely ne

Abstract: A method (100) for automated prediction of a clinical state, comprising: receiving (120) a set of parameter definitions for a clinical state, the set of parameter definitions comprising a definition for a deviation value, a definition for a deviation time, a definition for a value threshold, and a definition for a value time; receiving (130) a plurality of measurements for at least one feature for a patient, the plurality of measurements taken over a span of time; identifying (140), within the plurality of measurements for at least one feature for a patient, a deviation and/or an abnormality predicting the clinical state, comprising: predicting (150), based upon identification of a deviation and/or abnormality, that the patient is susceptible to or experiencing the clinical state; and providing (160), via a user interface of the event monitoring system, an alert that the patient is susceptible to or experiencing the clinical state.

Abstract: A method of implementing a task complexity learning system, including: learning a model for predicting the value of a continuous task variable y based upon an input variable x; learning an encoder that encodes a continuous task variable y into an encoded task value; calculating a loss function based upon the predicted value of y output by the model and the encoded task value output by the encoder; calculating a distortion function based upon the input continuous task variable y and the encoded task value, wherein learning the model and learning the encoder includes minimizing an objective function based upon the loss function and the distortion function for a set of input training data including x, y pairs.

Abstract: A mechanism or model for shock type classification that is able to differentiate between different types of shock, e.g. among patients with suspected hemodynamic instability. Respective one-versus-rest models are used to generate a numeric value for each of a plurality of shock types, each numeric value indicating a predicted probability that a subject exhibits that particular shock type over other types. A classification process is then performed to select the most likely shock type based on the numeric values.

Abstract: A method and system for predicting the next location for a patient in a healthcare facility, including: defining a location-procedure co-occurrence matrix for the healthcare facility, wherein the location-procedure co-occurrence matrix define the probability that a procedure will be performed in a specific location; defining a procedure transition matrix, wherein the procedure transition matrix defines the probability of moving from a first procedure to a second procedure; defining a patient input vector based upon the patient condition and procedures performed on the patient; calculating an output vector based upon the patient input vector and the procedure transition matrix; producing a procedure vector by setting all values in the output vector to zero except for the N highest values in the output vector, where N is an integer; calculating a location prediction vector based upon the procedure vector and the location-procedure co-occurrence matrix; and transmitting information regarding the M most likely ne

Abstract: A method of implementing a task complexity learning system, including: learning a model for predicting the value of a continuous task variable y based upon an input variable x; learning an encoder that encodes a continuous task variable y into an encoded task value; calculating a loss function based upon the predicted value of y output by the model and the encoded task value output by the encoder; calculating a distortion function based upon the input continuous task variable y and the encoded task value, wherein learning the model and learning the encoder includes minimizing an objective function based upon the loss function and the distortion function for a set of input training data including x, y pairs.

Abstract: An auditory signal processor includes a filter bank generating frequency components of a source audio signal; a spatial localization network operative in response to the frequency components to generate spike trains for respective spatially separated components of the source audio signal; a cortical network operative in response to the spike trains to generate a resultant spike train for selected spatially separated components of the source audio signal; and a stimulus reconstruction circuit that processes the resultant spike train to generate a reconstructed audio output signal for a target component of the source audio signal. The cortical network incorporates top-down attentional inhibitory modulation of respective spatial channels to produce the resultant spike train for the selected spatially separate components of the source audio signal, and the stimulus reconstruction circuit employs convolution of a reconstruction kernel with the resultant spike train to generate the reconstructed audio output.

Abstract: An auditory signal processor includes a filter bank generating frequency components of a source audio signal; a spatial localization network operative in response to the frequency components to generate spike trains for respective spatially separated components of the source audio signal; a cortical network operative in response to the spike trains to generate a resultant spike train for selected spatially separated components of the source audio signal; and a stimulus reconstruction circuit that processes the resultant spike train to generate a reconstructed audio output signal for a target component of the source audio signal. The cortical network incorporates top-down attentional inhibitory modulation of respective spatial channels to produce the resultant spike train for the selected spatially separate components of the source audio signal, and the stimulus reconstruction circuit employs convolution of a reconstruction kernel with the resultant spike train to generate the reconstructed audio output.