Abstract: Method and system of deploying a machine learning neural network (MLNN). The method comprises receiving a set of input features associated with data representative of a patient medical state at input layers of a trained MLNN, the trained MLNN comprising an output layer interconnected to the input layers via intermediate layers configured in accordance with an initial matrix of weights, a subset of the input features being activated responsive to a data sufficiency threshold reached in conjunction with deactivating, from the intermediate layers, a remainder of the input layers, the trained MLNN produced in accordance with adjusting the initial matrix of weights in diminishment of false positives in providing, at the output layer, a patient state diagnosis, and generating, at the output layer, a medical state diagnosis in accordance with the diminishment of false positives.

Abstract: Method and system of deploying a machine learning neural network (MLNN). The method comprises receiving a set of input features associated with data representative of a patient medical state at input layers of a trained MLNN, the trained MLNN comprising an output layer interconnected to the input layers via intermediate layers configured in accordance with an initial matrix of weights, a subset of the input features being activated responsive to a data sufficiency threshold reached in conjunction with deactivating, from the intermediate layers, a remainder of the input layers, the trained MLNN produced in accordance with adjusting the initial matrix of weights in diminishment of false positives in providing, at the output layer, a patient state diagnosis, and generating, at the output layer, a medical state diagnosis in accordance with the diminishment of false positives.

Abstract: Method and system of training a machine learning neural network (MLNN). The method comprises receiving a set of input features at respective input layers of the MLNN. The MLNN implemented in a processor and comprises an output layer interconnected to input layers via intermediate layers. The input features are associated with input feature data of a patient medical condition. Then selecting, responsive to a data qualification threshold level, a subset of the input layers while deactivating a remainder of the set of input layers. The intermediate layers are configured with an initial matrix of weights. Then training the MLNN based at least in part upon adjusting the initial matrix of weights based on a supervised classification that provides, via the output layer, one of negative and positive patient diagnostic states.

Abstract: Method and system of training a machine learning neural network (MLNN). The method comprises receiving a set of input features at respective input layers of the MLNN. The MLNN implemented in a processor and comprises an output layer interconnected to input layers via intermediate layers. The input features are associated with input feature data of a patient medical condition. Then selecting, responsive to a data qualification threshold level, a subset of the input layers while deactivating a remainder of the set of input layers. The intermediate layers are configured with an initial matrix of weights. Then training the MLNN based at least in part upon adjusting the initial matrix of weights based on a supervised classification that provides, via the output layer, one of negative and positive patient diagnostic states.