System, server and method for predicting adverse events

A method for predicting a medical outcome associated with a new event, the method comprising:

• training a Self-Organizing Map (SOM) to generate a trained model, wherein the trained model includes 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 input attributes of 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,
• 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;
• receiving a plurality of input attributes of the new event;
• performing pre-processing on the plurality of input attributes to generate an input data set;
• generating an output value from the trained model based upon the input data set;
• classifying the output value into an adverse event category to predict a medical outcome;
• and
• alerting and assigning an allocation of appropriate clinical resources to a patient based upon an optimization algorithm in accordance with the adverse event category of the new event and attributes of available clinical resources,
• wherein:
• one or more of the plurality of input attributes of the new event includes handover related data;
• wherein:
• the receiving of the plurality of input attributes of the new event further comprises receiving a plurality of new events, each including a plurality of input attributes;
• the generating of the output value and classifying the output value further includes generating a graphical image including input attributes for each of the plurality of new events;
• the method further comprises receiving a graphical display request from a remote client device and transmitting the graphical image to the remote client device as a response;
• and
• the graphical image is a cluster diagram including a plurality of clusters of new events having a similar characteristic.

An application specific integrated circuit (ASIC) for a neural network model (NNM), the ASIC comprising:

• a plurality of neurons organized in an array, wherein the plurality of neurons includes:
• an input layer including input neurons which provide input data set signals associated with an event to the NNM, wherein the event is a quantifiable medical outcome, and the plurality of input data set signals include a plurality of plurality of patient attributes associated with the quantifiable outcome;
• a hidden layer including hidden neurons;
• an output layer including output neurons which provide output data set signals;
• and
• a plurality of synaptic circuits, each synaptic circuit including a memory for storing a synaptic weight,
• where each of the hidden neurons and output neurons includes an activation function, the activation function is one of:
• (1) the sigmoid function ƒ(x)=1/(1+e−x);
• (2) the hyperbolic tangent function ƒ(x)=(e2x−1)/(e2x+1);
• and
• (3) a linear function ƒ(x)=x, wherein x is a summation of input neurons biased by the synoptic weights,
• wherein values of the synaptic weights are obtained by training the NNM, the training of the NNM includes:
• performing pre-processing on the plurality of input attributes for each of a plurality of past events to generate a plurality of input data sets;
• dividing the plurality of past events into a first set of training data and a second set of validation data;
• iteratively performing a machine learning algorithm (MLA) to update values of the synaptic weights of the NNM based upon the training data;
• and
• validating the NNM based upon the second set of validation data.