Abstract: A method for providing a computer-implemented medical diagnosis includes receiving an input from a user comprising at least one symptom of the user. The method also includes providing the at least one symptom as an input to a medical model, the medical model being retrieved from memory. The medical model includes a probabilistic graphical model comprising probability distributions and relationships between symptoms and diseases. The method also includes performing inference on the probabilistic graphical model to obtain a prediction of the probability that the user has that disease. The method also includes outputting an indication that the user has a disease from the Bayesian inference, wherein the inference is performed using a counterfactual measure.

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 method for providing a computer-implemented medical diagnosis includes receiving an input from a user comprising at least one symptom of the user. The method also includes providing the at least one symptom as an input to a medical model, the medical model being retrieved from memory. The medical model includes a probabilistic graphical model comprising probability distributions and relationships between symptoms and diseases. The method also includes performing inference on the probabilistic graphical model to obtain a prediction of the probability that the user has that disease. The method also includes outputting an indication that the user has a disease from the Bayesian inference, wherein the inference is performed using a counterfactual measure.

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: A method for providing a computer-implemented medical diagnosis includes receiving an input from a user comprising at least one symptom of the user. The method also includes providing the at least one symptom as an input to a medical model, the medical model being retrieved from memory. The medical model includes a probabilistic graphical model comprising probability distributions and relationships between symptoms and diseases. The method also includes performing inference on the probabilistic graphical model to obtain a prediction of the probability that the user has that disease. The method also includes outputting an indication that the user has a disease from the Bayesian inference, wherein the inference is performed using a counterfactual measure.

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 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: 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.