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 computer implemented method for developing a probabilistic graphical representation, the probabilistic graphical representation comprising nodes and links between the nodes indicating a relationship between the nodes, wherein the nodes represent conditions, the method comprising: using a language model to produce a context aware embedding for said condition; enhancing said embedding with one or more features to produce an enhanced embedded vector; and using a machine learning model to map said enhanced embedded vector to a value, wherein said value is related to the node representing said condition or a neighbouring node, wherein said machine learning model has been trained using said enhanced embedded vectors and observed values corresponding to said enhanced embedded vectors.

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 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: 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.