1. A computer implemented process for generating and interacting with a multi-factoral patient simulation responsive to user queries and interventions comprising the steps of: a.) using a computer to generate a simulated patient, said simulated patient comprising a patient age, gender and at least one medical condition affecting the health of said patient, said at least one medical condition including a temporal component defining said patient's medical history, wherein the step of using a computer to generate a simulated patient comprises the steps of: 1.) providing at least one first inference program describing a plurality of parallel health states of said simulated patient, wherein said plurality of parallel health states is a plurality of health states that exist simultaneously in said simulated patient; 2.) generating with said at least one first inference program a series of time-value pairs representative of a plurality of patient symptoms at an associated time; b.) accepting a general query from said user regarding said patient's health; c.) matching the general query to at least one query object most closely associated with the general query; d.) using a computer to generate a symptom history of said patient, wherein the symptom history is relevant to said patient's medical condition; e.) generating a report summarizing the results of the symptom history of step d, wherein the details of the symptom history may include the palliation, provocation, quality, radiation, and severity and duration for each symptom; f.) presenting said report generated in step e to said user; g.) accepting a detail query from said user; h.) identifying any previous interventions in said patient's medical history that are relevant to said detail query; i.) matching said detail query to at least one query object most closely associated thereto to determine a finding related to said query, wherein said finding includes at least one pattern list, said pattern list containing trigger interventions, doses, and time-value pairs representing a physiological parameter of said patient at some time; j.) comparing the previous interventions identified in step h with any trigger interventions identified in step i to determine whether any previous interventions are relevant to said user's detail query; k.) producing a list of interventions relevant to said user's query and a series of time-value pairs associated with each intervention in said list; l.) evaluating each time-value pair series in step k with a Bayesian network to determine the effects of said interventions on said patient; m.) presenting a report to said user comprising a list of said interventions and the effects thereof on said patient.