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.

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.

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.

Abstract: A method for providing a computer implemented medical diagnosis includes receiving an input from a user comprising a symptom of the user. The method also includes providing the symptom as an input to a medical model comprising a probabilistic graphical model comprising probability distributions and relationships between symptoms and diseases, and an inference engine configured to perform Bayesian inference on said probabilistic graphical model. The method also includes generating a question for the user to obtain further information concerning the user to allow a diagnosis, and outputting said question to the user. The method also includes outputting said question to the user, wherein generating a question for the user comprises ranking said questions by determining the utility of the possible questions.

Abstract: A computer implemented method of performing inference on a generative model, wherein the generative model in a probabilistic program form, said probabilistic program form defining variables and probabilistic relationships between variables, the method comprising: providing at least one of observations or interventions to the generative model; selecting an inference method, wherein the inference method is selected from one of: observational inference, interventional inference or counterfactual inference; performing the selected inference method using an approximate inference method on the generative model; and outputting a predicted outcome from the results of the inference; wherein approximate inference is performed by inputting an inference query and the model, observations, interventions and inference query are provided as independent parameters such that they can be iterated over and varied independently of each other.

Abstract: Methods for performing inference on a generative model are provided. In one aspect, a method includes receiving a generative model in a probabilistic program form defining variables and probabilistic relationships between variables, and producing a neural network to model the behaviour of the generative model. The input layer includes nodes corresponding to the variables of the generative model, and the output layer includes nodes corresponding to a parameter of the conditional marginal of the variables of the input layer. The method also includes training the neural network using samples from the probabilistic program. A loss function is provided for each node of the output layer. The loss function for each output node is independent of the loss functions for the other nodes of the output layer. The method also includes performing amortised inference on the generative model. Systems and machine-readable media are also provided.

Abstract: A method for providing a computer implemented medical diagnosis includes receiving an input from a user comprising a symptom of the user. The method also includes providing the symptom as an input to a medical model comprising a probabilistic graphical model comprising probability distributions and relationships between symptoms and diseases, and an inference engine configured to perform Bayesian inference on said probabilistic graphical model. The method also includes generating a question for the user to obtain further information concerning the user to allow a diagnosis, and outputting said question to the user. The method also includes outputting said question to the user, wherein generating a question for the user comprises ranking said questions by determining the utility of the possible questions.

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.

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.

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.