Koninklijke Philips Corporation N.V.

Boston University

Junzi Dong

Minnan Xu

Bryan Conroy

Liyi Xu

Harry Steven Colburn

Kenny Feng-Hsu Chou

Kamal Sen

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 next locations for the patient to a display device.

Location-procedure embedding based method for patient in-hospital location and procedure prediction

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.

System to identify and explore relevant predictive analytics tasks of clinical value and calibrate predictive model outputs to a prescribed minimum level of predictive accuracy

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.

Auditory signal processor using spiking neural network and stimulus reconstruction with top-down attention control

Growing community of inventors

Cambridge, MA, United States of America

Junzi Dong