Patent US10482385

receiving data corresponding to a plurality of patients including between 50 and 1,000,000 patients, the data including diagnostic information and/or treatment information for each patient;

parsing the data to generate normalized data for a plurality of variables including at least one variable related to diagnosis or treatment for each patient, wherein, for each patient, the normalized data is generated for more than one variable;

generating a causal relationship network model relating the plurality of variables based on the generated normalized data, the generating including creating and evolving an ensemble of Bayesian networks based on the generalized normalized data from between 50 patients and 1,000,000 patients, the causal relationship network model including variables related to a plurality of medical conditions; and

the causal relationship network generated using a programmed computing system including storage holding network model building code and one or more processors configured to execute the network model building code.

receiving a selection of a medical condition or a query associated with a medical condition from a plurality of medical conditions;

determining a sub-network from a computer generated causal relationship network model, the causal relationship network model generated by creating and evolving an ensemble of Bayesian networks based on data from between 50 patients and 1,000,000 patients and comprising a plurality of variables including variables related to the plurality of medical conditions, the causal relationship network model based on the selected medical conditions, the sub-network including one or more variables associated with the selected medical condition or the queried medical condition;

traversing the sub-network to identify one or more predictors for the selected medical condition or the queried medical condition; and

storing the one or more predictors for the selected medical condition or for the queried medical condition.

receiving information associated with a medical drug;

determining a sub-network from a computer generated causal relationship network model, the causal relationship network model generated by creating and evolving an ensemble of Bayesian networks based on data from between 50 patients and 1,000,000 patients and comprising a plurality of variables including variables related to a plurality of medical drugs, the causal relationship network model based on the medical drug, the sub-network including one or more variables associated with the medical drug;

traversing the sub-network to identify one or more predictors for the medical drug; and

 

storing the one or more predictors for the medical drug.

a data-receiving module configured to receive data related to a plurality of patients including between 50 and 1,000,000 patients, the data including diagnostic information and/or treatment information for each patient;

a parsing module configured to parse the data to generate normalized data for a plurality of variables, wherein, for each patient, the normalized data is generated for more than one variable; and

a processor-implemented relationship-network module configured to generate a causal relationship network model relating the plurality of variables based on the generated normalized data, the generating including creating and evolving an ensemble of Bayesian networks based on the generalized normalized data from between 50 patients and 1,000,000 patients, the causal relationship network model including variables related to a plurality of medical conditions.