A system rapidly produces training cases for machine based learning by automatically creating training cases from a database of historical data. The system determines a plurality of attributes relevant to each of the training cases. The system identifies a first attribute of the plurality of attributes as an issue, and a second attribute of the plurality attributes as a response to the issue. The system identifies a plurality of cohort members from the database of historical data, where each cohort member comprises cohort member attributes that match a subset of the plurality of attributes. The system analyzes the cohort member attributes of each of the plurality of cohort members to identify the most frequent responses to the issue. The system creates the training cases where each training case comprises the issue and the most frequent responses. The system then trains a machine based learning system using the training cases.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of rapidly producing training cases for machine based learning, the method comprising: automatically creating one or more training cases for machine based learning from a database of historical data by: determining a plurality of attributes relevant to each of the training cases; identifying a first attribute of the plurality of attributes as an issue; identifying a second attribute of the plurality attributes as a response to the issue; identifying a plurality of cohort members from the database of historical data, wherein each cohort member comprises cohort member attributes that match a subset of the plurality of attributes; analyzing the cohort member attributes of each of the plurality of cohort members to identify a plurality of most frequent responses to the issue; and creating the one or more training cases wherein each training case comprises the issue and the plurality of most frequent responses; and training a machine based learning system using the one or more training cases. 2. The method of claim 1 further comprising: updating at least one of the plurality of cohort members and the cohort member attributes as new data is inserted into the database of historical data. 3. The method of claim 1 further comprising: continuing to train the machine based learning system as at least one of: i) new data is inserted into the database of historical data; and ii) at least one additional database of historical data is added to the database of historical data. 4. The method of claim 1 wherein automatically creating the one or more training cases for machine based learning from the database of historical data comprises: selecting the database of historical data from a plurality of databases of historical data to produce training cases that reflect a consensus associated with the selected database. 5. The method of claim 1 wherein automatically creating the one or more training cases for machine based learning from the database of historical data comprises: compiling the database of historical data from a plurality of databases to reflect a consensus associated with the plurality of databases. 6. The method of claim 1 wherein identifying the second attribute of the plurality attributes as the response to the issue comprises: identifying the issue as having a least two suitable responses. 7. The method of claim 1 wherein identifying the plurality of cohort members from the database of historical data, wherein each cohort member comprises cohort member attributes that match a subset of the plurality of attributes comprises: determining a number of attributes relevant to the training case that will result in suitable responses to the issue. 8. The method of claim 1 wherein identifying the plurality of cohort members from the database of historical data, wherein each cohort member comprises cohort member attributes that match a subset of the plurality of attributes comprises: defining a range of values associated with at least one of the cohort member attributes that match at least one of the plurality of attributes. 9. The method of claim 1 wherein analyzing the cohort member attributes of each of the plurality of cohort members to identify the plurality of most frequent responses to the issue comprises: ranking each of the plurality of most frequent responses within the plurality of most frequent responses. 10. The method of claim 1 wherein analyzing the cohort member attributes of each of the plurality of cohort members to identify the plurality of most frequent responses to the issue comprises: identifying a threshold of suitable responses to the issue; and identifying the plurality of most frequent responses as those responses that meet or exceed the threshold. 11. The method of claim 1 wherein creating the one or more training cases wherein each training case comprises the issue and the plurality of most frequent responses comprises: identifying the historical database as a database of patient medical records; identifying the issue as a medical issue; identifying the response as a therapy for the medical issue; and training the machine based learning system to determine suitable therapies for the medical issue. 12. The method of claim 11 wherein training the machine based learning system to determine suitable therapies for the medical issue comprises: training the machine based learning system to determine suitable therapies for the medical issue, wherein the suitable therapies reflect a consensus among medical providers while reducing medical provider bias. 13. A computer program product for rapidly producing training cases for machine based learning, the computer program product comprising: a computer readable storage medium having computer readable program code embodied therewith, the program code executable by a computing processor to: automatically create one or more training cases for machine based learning from a database of historical data by: determining a plurality of attributes relevant to each of the training cases; identifying a first attribute of the plurality of attributes as an issue; identifying a second attribute of the plurality attributes as a response to the issue; identifying a plurality of cohort members from the database of historical data, wherein each cohort member comprises cohort member attributes that match a subset of the plurality of attributes; analyzing the cohort member attributes of each of the plurality of cohort members to identify a plurality of most frequent responses to the issue; and creating the one or more training cases wherein each training case comprises the issue and the plurality of most frequent responses; and train a machine based learning system using the one or more training cases. 14. The computer program product of claim 13 wherein the computer readable program code configured to identify the plurality of cohort members from the database of historical data, wherein each cohort member comprises cohort member attributes that match a subset of the plurality of attributes is further configured to: define a range of values associated with at least one of the cohort member attributes that match at least one of the plurality of attributes. 15. The computer program product of claim 13 wherein the computer readable program code configured to analyze the cohort member attributes of each of the plurality of cohort members to identify the plurality of most frequent responses to the issue is further configured to: identify a threshold of suitable responses to the issue; and identify the plurality of most frequent responses as those responses that meet or exceed the threshold. 16. The computer program product of claim 13 wherein the computer readable program code configured to create the one or more training cases wherein each training case comprises the issue and the plurality of most frequent responses is further configured to: identify the historical database as a database of patient medical records; identify the issue as a medical issue; identify the response as a therapy for the medical issue; and train the machine based learning system to determine suitable therapies for the medical issue. 17. A system comprising: a computing processor; and a computer readable storage medium operationally coupled to the processor, the computer readable storage medium having computer readable program code embodied therewith to be executed by the computing processor, the computer readable program code configured to: automatically create one or more training cases for machine based learning from a database of historical data by: determining a plurality of attributes relevant to each of the training cases; identifying a first attribute of the plurality of attributes as an issue; identifying a second attribute of the plurality attributes as a response to the issue; identifying a plurality of cohort members from the database of historical data, wherein each cohort member comprises cohort member attributes that match a subset of the plurality of attributes; analyzing the cohort member attributes of each of the plurality of cohort members to identify a plurality of most frequent responses to the issue; and creating the one or more training cases wherein each training case comprises the issue and the plurality of most frequent responses; and train a machine based learning system using the one or more training cases. 18. The system of claim 17 further configured to: update at least one of the plurality of cohort members and the cohort member attributes as new data is inserted into the database of historical data. 19. The system of claim 17 further configured to: continue to train the machine based learning system as at least one of: i) new data is inserted into the database of historical data; and ii) at least one additional database of historical data is added to the database of historical data. 20. The system of claim 17 wherein the computer readable program code configured to identify the plurality of cohort members from the database of historical data, wherein each cohort member comprises cohort member attributes that match a subset of the plurality of attributes is further configured to: determine a number of attributes relevant to the training case that will result in suitable responses to the issue.