Counterfactual measure for medical diagnosis

A system for providing a computer implemented medical diagnosis, the system comprising a processor and a memory, the processor being adapted to:

• receive an input from a user comprising at least one symptom of the user;
• provide the at least one symptom as an input to a medical model, the medical model being retrieved from memory, the medical model comprising:
• a probabilistic graphical model being stored in memory comprising probability distributions and causal relationships between symptoms and diseases,
• perform counterfactual inference on the probabilistic graphical model to obtain a prediction of the probability that the user has a disease;
• and
• output a counterfactual measure determined from the counterfactual inference, wherein, the counterfactual measure is based on whether one or more symptoms would be expected to be present if a first one or more diseases were cured,
• wherein the probabilistic graphical model is a twin network, the twin network formed by:
• creating a first graphical representation from a set of data, the first graphical representation comprising a plurality of first nodes indicating symptoms, risk factors and diseases, the first graphical representation indicating a relationship between the symptoms, the risk factors and the diseases, the first graphical representation indicating real variables;
• creating a second graphical representation, wherein the second graphical representation is a copy of the first graphical representation, the second graphical representation comprising a plurality of second nodes indicating the symptoms, the risk factors and the diseases, the second graphical representation indicating counterfactual states of the real variables;
• and
• combining the first graphical representation and the second graphical representation by the sharing of exogenous causes to create said twin network.

A non-transitory carrier medium carrying computer readable instructions being adapted to cause a computer to perform:

• receiving an input from a user comprising at least one symptom of the user;
• providing the at least one symptom as an input to a medical model, the medical model being retrieved from memory, the medical model comprising:
• a probabilistic graphical model comprising probability distributions and causal relationships between symptoms and diseases;
• performing counterfactual inference on the probabilistic graphical model to obtain a prediction of the probability that the user has a disease;
• and
• outputting a counterfactual measure determined from the counterfactual inference, wherein, the counterfactual measure is based on whether one or more symptoms would be expected to be present if a first one or more diseases were cured,
• wherein the probabilistic graphical model is a twin network, the twin network formed by:
• creating a first graphical representation from a set of data, the first graphical representation comprising a plurality of first nodes indicating symptoms, risk factors and diseases, the first graphical representation indicating a relationship between the symptoms, the risk factors and the diseases, the first graphical representation indicating real variables;
• creating a second graphical representation, wherein the second graphical representation is a copy of the first graphical representation, the second graphical representation comprising a plurality of second nodes indicating the symptoms, the risk factors and the diseases, the second graphical representation indicating counterfactual states of the real variables;
• and
• combining the first graphical representation and the second graphical representation by the sharing of exogenous causes to create said twin network.