Babylon Partners Limited

Ciarán Mark Lee

Saurabh Johri

Jonathan George Richens

Christopher Robert Hart

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.

Counterfactual measure for medical diagnosis

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.

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London, United Kingdom

Ciarán Mark Lee