System, method and device for predicting need for medical treatment
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.
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.
2. The method of claim 1 , wherein:
the NNM includes an input layer, output layer, and a plurality of hidden layers with a plurality of hidden neurons; and
each of the plurality of hidden 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)=(e 2x −1)/(e 2x +1); and
(3) a linear function ƒ(x)=x,
wherein x is a summation of input neurons biased by the synaptic weights.
3. The method of claim 1 , wherein the NNM is one or more of a feed forward structure Neural Network; Convolutional neural network (CNN); ADALINE Neural Network, Adaptive Resonance Theory 1 (ART1), Bidirectional Associative Memory (BAM), Boltzmann Machine, Counterpropagation Neural Network (CPN), Elman Recurrent Neural Network, Hopfield Neural Network, Jordan Recurrent Neural Network, Neuroevolution of Augmenting Topologies (NEAT), and Radial Basis Function Network.
4. The method of claim 1 , wherein the performing of the MLA includes measuring a global error in each training iteration for the NNM by:
calculating a local error, the local error being a difference between the output value of the NNM and the quantifiable outcome;
calculating the global error by summing all of the local errors in accordance with one of: (1) Mean Square Error (MSE) formula
∑
n
E
2
n
;
(2) Root Mean Square Error (RMS) formula
∑
n
E
2
n
;
and
(3) Sum of Square Errors (ESS) formula
∑
n
E
2
2
,
wherein n represents a total number of the past patient events and E represents the local error.
5. The method of claim 1 , wherein the plurality of segmented images of the patient are from an imaging study.
6. The method of claim 5 , wherein the plurality of input attributes include a plurality of Hounsfield unit (HU) values associated with the imaging study.
7. The method of claim 6 , wherein the imaging study includes a computed tomography (CT) scan of the head of the patient using series of x-rays of a head of the patient taken from many different directions.
8. The method of claim 1 , wherein:
the plurality of segmented images of the patient are computed tomography (CT) scan images;
the plurality of input attributes includes an array of pixel intensity values associated with each of the plurality of segmented images;
the performing of pre-processing on the plurality of input attributes to generate an input data set further includes windowing values of the pixel intensity values outside of a predetermined range; and
the generating of the representation of the reconstructed image from the plurality of segmented images is generated based upon pixel intensity values in each of the segmented images.
9. The method of claim 1 , wherein:
the plurality of input attributes includes characteristics of a treated patient; and
the generating of the medical output value from the trained model based upon the input data set further includes generating a patient treatment plan.
10. The method of claim 1 , wherein:
the plurality of input attributes includes characteristics of a treated patient; and
the generating of the medical output value from a trained model based upon the input data set further includes generating a patient diagnosis and a patient treatment plan for the patient diagnosis.