US 12,451,229 B1
System, method and device for predicting need for medical treatment
David LaBorde, Alpharetta, GA (US)
Assigned to Brain Trust Innovations I, LLC, Alpharetta, GA (US)
Filed by Brain Trust Innovations I, LLC, Alpharetta, GA (US)
Filed on May 10, 2023, as Appl. No. 18/314,819.
Application 18/314,819 is a continuation in part of application No. 16/139,584, filed on Sep. 24, 2018, granted, now 11,961,619.
Claims priority of provisional application 62/575,332, filed on Oct. 20, 2017.
This patent is subject to a terminal disclaimer.
Int. Cl. G16H 20/40 (2018.01); G16H 30/20 (2018.01); G16H 40/20 (2018.01); G16H 50/20 (2018.01)
CPC G16H 20/40 (2018.01) [G16H 30/20 (2018.01); G16H 40/20 (2018.01); G16H 50/20 (2018.01)] 10 Claims
OG exemplary drawing
 
1. A computer implemented method for generating a medical output value associated with a new event, the new event associated with a plurality of segmented images of a patient, the method comprising: further comprising:
storing a plurality of past events, each of the plurality of past events including a plurality of past input attributes and a quantifiable outcome; and
training a neural network model (NNM) to generate a trained model, wherein the training of the NNM includes:
performing pre-processing on the plurality of past input attributes for each of the plurality of past events to generate a plurality of past 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 synaptic weights of the NNM based upon the training data; and
validating the NNM based upon the second set of validation data; 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 a representation of a reconstructed image from the plurality of segmented images; and
generating the medical output value including word text from the trained model based upon the representation of the reconstructed image.