Patent ID: 11961619
Assignee: BRAIN TRUST INNOVATIONS I, LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 11:
12. A method for predicting an outcome associated with a new patient event, the method comprising:
receiving a plurality of input attributes of the new patient event;
performing pre-processing on the plurality of input attributes to generate an input data set;
generating an output value from a trained model based upon the input data set, wherein the trained model is a trained Self-Organizing Map (SOM) including a plurality of network nodes arranged in a grid or lattice and in fixed topological positions, an input layer with a plurality of input nodes representing the input attributes of the past patient events, wherein each of the plurality of input nodes is connected to all of the plurality of network nodes by a plurality of synaptic weights;
storing a plurality of past patient events, each of the plurality of past patient events including a plurality of input attributes and a quantifiable outcome;
performing pre-processing on the plurality of input attributes for each of the plurality of past patient events to generate a plurality of input data sets; and
training a SOM to generate the trained model, wherein the training of the SOM includes:
initializing values of the plurality of synaptic weights to random values,
randomly selecting one past patient event and determining which of the plurality of network nodes is a best matching unit (BMU) according to a discriminant function, wherein the discriminant function is a Euclidean Distance; and
iteratively calculating a neighborhood radius associated with the BMU to determine neighboring network nodes for updating, and updating values of synoptic weights for neighboring network nodes within the calculated neighborhood radius for a fixed number of iterations to generate the trained model; and

classifying the output value into a surgical intervention risk category to predict the outcome.