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: A computer-implemented method of generating a PGM with causal information, said graphical model containing the causal relationship between a first variable and a second variable, the method comprising: receiving data at a processor, said data showing a correlation between the first variable and a second variable; producing a third variable by reducing the dimensionality of the graphical representation of the two dimensional data defined by the first variable and the second variable, determining determine the causal relationship between the first and third variables and the second and third variable, the causal discovery algorithm being able to determine if the first variable causes the third variable, the third variable causes the first variable, the second variable causes the third variable and the third variable causes the second variable; and outputting a graphical model indicating the direction of edges in a graphical representation of said PGM.

Abstract: A computer-implemented method of generating a PGM with causal information, said graphical model containing the causal relationship between a first variable and a second variable, the method comprising: receiving data at a processor, said data showing a correlation between the first variable and a second variable; producing a third variable by reducing the dimensionality of the graphical representation of the two dimensional data defined by the first variable and the second variable, determining determine the causal relationship between the first and third variables and the second and third variable, the causal discovery algorithm being able to determine if the first variable causes the third variable, the third variable causes the first variable, the second variable causes the third variable and the third variable causes the second variable; and outputting a graphical model indicating the direction of edges in a graphical representation of said PGM.

Abstract: Computer implemented methods and systems of using a trained probabilistic graphical model to predict whether a user will develop a health condition are provided. The method includes retrieving data concerning the user, inputting the retrieved data into a trained model, the trained model being a probabilistic graphical model comprising an observable variable space, a latent variable space and an outcome relating to said condition, wherein the observable multidimensional variable space is dependent on the multidimensional latent variable space and the likelihood of a user developing a condition is dependent on the multidimensional latent variable space, wherein the trained model has been trained using observational training data wherein said observational training data comprises observations regarding individuals developing said condition, and using said trained model to output if and when the user is likely to develop the condition.

Abstract: Computer implemented methods and systems of using a trained probabilistic graphical model to predict whether a user will develop a health condition are provided. The method includes representing functional dependencies of first and second statistical models as neural networks. The method also includes receiving training data comprising time to event data with corresponding intervention data and observable variables. The method also includes training said neural networks using said training data.

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.