A computer implemented determination method and system

Abstract

Methods for providing a computer implemented medical diagnosis are provided. In one aspect, a method includes receiving an input from a user comprising at least one symptom of the user, and providing the at least one symptom as an input to a medical model. The method also includes deriving estimates of the probability of the user having a disease from the discriminative model, inputting the estimates to the inference engine, performing approximate inference on the probabilistic graphical model to obtain a prediction of the probability that the user has that disease, and outputting the probability of the user having the disease for display by a display device.

1 . A method for providing a computer implemented medical diagnosis, the method comprising:
 receiving an input from a user comprising at least one symptom of the user;   providing the at least one symptom as an input to a medical model comprising:
 a probabilistic graphical model comprising probability distributions and relationships between symptoms and diseases; 
 an inference engine configured to perform Bayesian inference on said probabilistic graphical model; and 
 a discriminative model pre-trained to approximate the probabilistic graphical model, the discriminative model being trained using samples from said probabilistic graphical model, wherein some of the data of the samples has been masked to allow the deterministic model to produce data which is robust to the user providing incomplete information about their symptoms; 
   deriving estimates, from the discriminative model, of the probability of the user having a disease;   inputting the estimates to the inference engine;   performing approximate inference on the probabilistic graphical model to obtain a prediction of the probability that the user has that disease; and   outputting the probability of the user having the disease for display by a display device.   
     
     
         2 . A method according to  claim 1 , wherein the inference engine is adapted to perform importance sampling over conditional marginals. 
     
     
         3 . A method according to  claim 1 , wherein the discriminative model is a neural network. 
     
     
         4 . A method according to  claim 3 , wherein the neural network is a neural network that can approximate the outputs of the probabilistic graphical model. 
     
     
         5 . A method according to  claim 3 , wherein the neural network is a neural network comprising a plurality of sub-networks that can approximate the outputs of the probabilistic graphical model. 
     
     
         6 . A method according to  claim 3 , wherein the neural network is a single neural network that can approximate the outputs of the probabilistic graphical model. 
     
     
         7 . A method according to  claim 1 , wherein the probabilistic graphical model is a noisy-OR model. 
     
     
         8 . A method according to  claim 1 , wherein determining the probability that the user has one or more diseases further comprises determining whether further information from the user would improve the diagnosis and requesting further information. 
     
     
         9 . A method according to  claim 1 , wherein the medical model receives information concerning the symptoms of the user and risk factors of the user. 
     
     
         10 .- 17 . (canceled) 
     
     
         18 . A non-transitory carrier medium comprising computer readable code configured to cause a computer to perform the method of  claim 1 . 
     
     
         19 . A system for providing a computer implemented medical diagnosis, the system comprising:
 a user interface for receiving an input from a user comprising at least one symptom of the user;   a processor, said processor being configured to:
 provide the at least one symptom as an input to a medical model comprising:
 a probabilistic graphical model comprising probability distributions and relationships between symptoms and diseases; 
 an inference engine configured to perform Bayesian inference on said probabilistic graphical model; and 
 a discriminative model pre-trained to approximate the probabilistic graphical model, the discriminative model being trained using samples from said probabilistic graphical model, wherein some of the data of the samples has been masked to allow the deterministic model to produce data which is robust to the user providing incomplete information about their symptoms; 
 
 derive estimates, from the discriminative model, of the probability of the user having a disease; 
 input the estimates to the inference engine; and 
 perform approximate inference on the probabilistic graphical model to obtain a prediction of the probability that the user has that disease, 
   
       the system further comprising a display device adapted to display the probability of the user having the disease. 
     
     
         20 .- 22 . (canceled) 
     
     
         23 . A method for providing a computer implemented determination process for determining a probable cause from a plurality of causes, the method comprising:
 receiving an input from a user comprising an observation;   providing the at least one observation as an input to a determination model comprising:
 a probabilistic graphical model comprising probability distributions and relationships between observations and causes; 
 an inference engine configured to perform Bayesian inference on said probabilistic graphical model; and 
 a discriminative model pre-trained to approximate the probabilistic graphical model, the discriminative model being trained using samples from said probabilistic graphical model, wherein some of the data of the samples has been masked to allow the deterministic model to produce data which is robust to the user providing incomplete information about the observations; 
   deriving estimates, from the discriminative model, of the probability of the most probable cause of the observations;   inputting the estimates to the inference engine;   performing approximate inference on the probabilistic graphical model to obtain a prediction of the most probable cause based on the observations; and   
       outputting the probability of the most probable cause for the inputted observations for display by a display device. 
     
     
         24 . (canceled) 
     
     
         25 . A system according to  claim 19 , wherein the processor comprises a graphical processing unit. 
     
     
         26 . A method according to  claim 23 , wherein the inference engine is adapted to perform importance sampling over conditional marginals. 
     
     
         27 . A method according to  claim 23 , wherein the discriminative model is a neural network. 
     
     
         28 . A method according to  claim 27 , wherein the neural network is a neural network that can approximate the outputs of the probabilistic graphical model. 
     
     
         29 . A method according to  claim 27 , wherein the neural network is a neural network comprising a plurality of sub-networks that can approximate the outputs of the probabilistic graphical model. 
     
     
         30 . A method according to  claim 27 , wherein the neural network is a single neural network that can approximate the outputs of the probabilistic graphical model. 
     
     
         31 . A method according to any  claim 23 , wherein the probabilistic graphical model is a noisy-OR model. 
     
     
         32 . A non-transitory carrier medium comprising computer readable code configured to cause a computer to perform the method of  claim 23 .