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US 11,961,619 B1Granted
System, method and device for predicting need for surgical intervention
AssigneeBrain Trust Innovations I, LLC
InventorDavid LaBorde
Life of this patent
Estimating the term…
Abstract
A system that includes data collection engine devices, client devices and backend devices. The backend devices include trained models, business logic, and attributes of a plurality of patient events. A plurality of data collection engines and hospital information systems send input attributes of new patient events to the backend devices. The backend devices can predict particular outcomes of new patient events based upon the input attributes utilizing the trained models.
Claims16
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;
- classifying the output value into a surgical intervention risk category to predict the outcome;
- 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;
- and
- training a neural network model (NNM) to generate the trained model, wherein the training of the NNM includes:
- 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;
- dividing the plurality of past patient events into a first set of training data and a second set of validation data;
- iteratively performing a machine learning algorithm (MLA) to update synaptic weights of the NNM based upon the training data;
- and
- validating the NNM based upon the second set of validation data.
9 more elements
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.
10 more elements
A computer implemented method for predicting a diagnosis associated with a new patient event based upon computerized tomography (CT) scan images, the method comprising:
- receiving a plurality of input attributes including intensity values in Hounsfield units of pixels in the CT scan images;
- performing pre-processing on the plurality of input attributes to generate an input data set;
- generating an output value from a trained model stored in a non-transitory storage device associated with a server device based upon the input data set;
- predicting the diagnosis based upon the output value;
- 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;
- training a neural network model (NNM) to generate the trained model, wherein the training of the NNM includes:
- 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;
- dividing the plurality of past patient events into a first set of training data and a second set of validation data;
- iteratively performing a machine learning algorithm (MLA) to update synaptic weights of the NNM based upon the training data;
- and
- validating the NNM based upon the second set of validation data.
8 more elements
A computer implemented 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, at a server device, pre-processing on the plurality of input attributes to generate an input data set;
- generating an output value from a trained model stored in a non-transitory storage device associated with the server device based upon the input data set;
- classifying the output value into a surgical intervention risk category to predict the outcome;
- 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;
- training a neural network model (NNM) to generate the trained model, wherein the training of the NNM includes:
- 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;
- dividing the plurality of past patient events into a first set of training data and a second set of validation data;
- iteratively performing a machine learning algorithm (MLA) to update synaptic weights of the NNM based upon the training data;
- and
- validating the NNM based upon the second set of validation data.
8 more elements
Dependencies are inferred from the claim text.