Systems, methods, and computer-readable medium are provided for healthcare analysis. Data corresponding to a plurality of patients is received. The data is parsed to generate normalized data for a plurality of variables, with normalized data generated for more than one variable for each patient. A causal relationship network model is generated relating the plurality of variables based on the generated normalized data using a Bayesian network algorithm. The causal relationship network model includes variables related to a plurality of medical conditions or medical drugs. In another aspect, a selection of a medical condition or drug is received. A sub-network is determined from a causal relationship network model. The sub-network includes one or more variables associated with the selected medical condition or drug. One or more predictors for the selected medical condition or drug are identified.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for generating a causal relationship network model based on patient data, the method comprising: receiving data corresponding to a plurality of 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, 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 using a Bayesian network algorithm, 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. 2. The method of claim 1, wherein the causal relationship network model includes relationships indicating one or more predictors for each of the plurality of medical conditions 3. The method of claim 1, wherein the data received is not pre-selected as being relevant to one or more of the plurality of medical conditions. 4. The method of claim 1, wherein the plurality of patients includes a first subset of patients each having data indicating a diagnosis of a medical condition in the patient and a second subset of patients each having data that does not indicate a diagnosis of a medical condition in the patient. 5. The method of claim 1, further comprising: receiving additional data corresponding to one or more additional patients; and updating the causal relationship network model based on the additional data. 6. The method of claim 1, further comprising: receiving updated or additional data corresponding to one or more of the plurality of patients; and updating the casual relationship network model based on the updated or additional data. 7. The method of claim 1, wherein the causal relationship network model is generated based solely on the generated normalized data. 8. The method of claim 1, further comprising: determining a sub-network from the casual relationship network model, one or more variables in the sub-network associated with a selected medical condition; and probing relationships in the sub-network to determine one or more predictors for the selected medical condition. 9. The method of claim 8, wherein the one or more predictors for the selected medical condition indicate a medical condition co-occurring with the selected medical condition. 10. The method of claim 8, wherein the extent of the sub-network is determined based on the one or more variables associated with the selected medical condition and the strength of the relationships between the one or more variables and other variables in the causal relationship network model. 11. The method of claim 8, wherein the sub-network includes the one or more variables associated with the selected medical condition, a first set of additional variables each having a first degree relationship with the one or more variables, and a second set of additional variables each having second degree relationship with the one or more variables. 12. The method of claim 8, wherein at least one of the one or more predictors is previously unknown as a predictor for the selected medical condition. 13. The method of claim 8, wherein at least one of the one or more predictors is newly identified as a predictor for the medical condition. 14. The method of claim 8, wherein the number of predictors is less than the number of variables. 15. The method of claim 8, further comprising: displaying the one or more predictors in a user interface, the displaying including a graphical representation of the one or more variables, the one or more predictors, and relationships among the one or more variables and the one or more predictors. 16. The method of claim 8, further comprising displaying a graphical representation of the sub-network in a user interface. 17. The method of claim 8, further comprising ranking the one or more predictors based on strength of relationships between the one or more variables and the one or more predictors. 18. The method of claim 1, further comprising: determining a sub-network from the causal relationship network model, one or more variables in the sub-network associated with a selected drug; and probing the sub-network to determine one or more predictors relevant to the selected drug. 19. The method of claim 18, wherein the one or more predictors relevant to the selected drug indicates a drug administered in conjunction with the selected drug. 20. The method of claim 18, wherein the one or more predictors indicate an adverse drug interaction between the selected drug and one or more other drugs. 21. The method of claim 20, wherein at least one of the one or more predictors is newly identified as a predictor for an adverse drug interaction between the selected drug and the one or more other drugs. 22. The method of claim 18, wherein the extent of the sub-network is determined based on the one or more variables associated with the selected drug and the strength of the relationships between the one or more variables and other variables in the causal relationship network model. 23. The method of claim 18, wherein the sub-network includes the one or more variables associated with the selected drug, a first set of additional variables each having a first degree relationship with the one or more variables, and a second set of additional variables each having second degree relationship with the one or more variables. 24. The method of claim 18, wherein at least one of the one or more predictors is previously unknown as a predictor for the selected drug. 25. The method of claim 18, wherein the number of predictors is less than the number of variables. 26. The method of claim 1, wherein the casual relationship network model is generated based on at least 50 variables. 27.-30. (canceled) 31. The method of claim 1, wherein the causal relationship network is generated based on data from between 50 patients and 1,000,000 patients. 32. The method of claim 1, wherein the data includes information from patient electronic health records. 33. The method of claim 1, wherein the received data further includes at least one of: patient demographics, medical history, patient family medical history, active medication information, inactive past medication information, allergy information, immunization status information, laboratory test results, radiology images, vital sign information, patient weight, billing information, life style information, habits information, insurance claims information, and pharmacy information, for at least some of the plurality of patients. 34. The method of claim 33, wherein the patient demographics includes at least one of: patient age, patient race, and patient ethnicity. 35. The method of claim 1, wherein the received data includes information from patient chart. 36. The method of claim 35, wherein information from the patient chart includes at least one of: notes by a health care professional, observations by a health care professional, administration of drugs and therapies, orders for the administration of drugs and therapies, test results, and x-rays. 37. The method of claim 1, wherein the received data includes patient discharge information. 38. The method of claim 37, wherein the patient discharge information includes at least one of: a diagnosis code, a treatment code, an insurance charge code, a diagnosis-related group code, and an International Classification of Diseases code. 39. The method of claim 1, wherein the received data relates to a plurality of patients from a selected hospital. 40. The method of claim 1, wherein the received data relates to a plurality of patients from a selected geographic area. 41. The method of claim 1, wherein generating the causal relationship network model relating the variables for the plurality of patients based on the generated normalized data using a Bayesian network algorithm comprises: creating a library of network fragments based on the variables via a Bayesian Fragment Enumeration process; creating an ensemble of trial networks, each trial network constructed from a different subset of the network fragments in the library; and globally optimizing the ensemble of trial networks by evolving each trial network through local transformations via simulated annealing to produce a consensus causal relationship network model. 42. The method of claim 41, wherein generating the causal relationship network model relating the variables for the plurality of patients based on the generated normalized data using a Bayesian network algorithm further comprises: in silico simulation of the consensus causal relationship network model based on input data, to provide a confidence level of prediction for one or more casual relationships within the resulting causal relationship network model. 43. A computer-implemented method for using a causal relationship network model, the method comprising: 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 from patient data using a Bayesian network algorithm and comprising a plurality of variables including variables related to the plurality of medical conditions, the casual 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. 44. The method of claim 43, wherein the selection of the medical condition or the query associated with the medical condition is received from a user via a user interface. 45. The method of claim 43, wherein at least one of the one or more predictors is previously unknown as a predictor for the selected medical condition or for the queried medical condition. 46. The method of claim 43, wherein at least one of the one or more predictors is newly identified as a predictor for the selected medical condition or for the queried medical condition. 47. The method of claim 43, wherein the number of predictors is less than the number of variables. 48. The method of claim 43, further comprising: displaying the predictors in the user interface, the displaying including a graphical representation of the one or more selected variables, the one or more predictors, and relationships among the one or more selected variables and the predictors. 49. The method of claim 43, further comprising displaying a graphical representation of the sub-network in the user interface. 50. The method of claim 43, further comprising ranking the one or more predictors based on strength of relationships between the one or more selected variables and the one or more predictors. 51. The method of claim 43, wherein the one or more predictors are associated with one or more medical drugs. 52. The method of claim 43, wherein the predictors are associated with one or more medical conditions. 53. (canceled) 54. The method of claim 43, wherein the query includes information associated with a medical condition and/or a medical drug. 55. A computer-implemented method for using a causal relationship network model, the method comprising: 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 from patient data using a Bayesian network algorithm and comprising a plurality of variables including variables related to the plurality of medical drugs, the casual 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. 56. A system for generating a causal relationship network model based on patient data, the system comprising: a data-receiving module configured to receive data related to a plurality of 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 using a Bayesian network algorithm, the causal relationship network model including variables related to a plurality of medical conditions. 57. The system of claim 56, wherein the casual relationship network model includes relationships indicating one or more predictors for each of the plurality of medical conditions. 58. A system for using a causal relationship network model based on patient data, the system comprising: a data-receiving module configured to receive information associated with a medical condition; a sub-network module configured to determine a sub-network from a computer generated causal relationship network model, the causal relationship network model generated from patient data using a Bayesian network algorithm and comprising a plurality of variables including variables related to a plurality of medical conditions, the casual relationship network model based on the medical condition, the sub-network including one or more variables associated with the medical condition; and a variable identification module configured to traverse the sub-network and identify one or more predictors for the medical condition. 59. The system of claim 58, wherein the computer-generated causal relationship network model was generated using the method of claim 1. 60. A non-transitory machine readable storage medium storing at least one program that, when executed by at least one processor, causes the at least one processor to perform the method of claim 1. 61.-62. (canceled) 63. The system of claim 56, further comprising: a sub-network selection module configured to: receive information associated with a medical condition from a user via a user interface; determine a sub-network from the causal relationship network model, the sub-network including one or more variables related to the medical condition; traverse the sub-network to identify one or more predictors for the medical condition; and store the one or more predictors for the medical condition. 64. (canceled)