Methods and systems for causative chaining of prognostic label classifications

1. A system for causative chaining of prognostic label classifications, the system comprising:

at least a computing device, the computing device designed and configured to:

receive training data, wherein receiving the training data further comprises:

receiving a first training set including a plurality of first data entries, each first data entry of the plurality of first data entries including at least a first element of physiological state data and at least a correlated first prognostic label;

receive a list of significant categories of physiological state data;

associate the at least correlated first prognostic label with at least a category from the list of significant categories of prognostic labels, as a function of receiving the first training data set and the list of significant categories, utilizing a language processing model comprising a program generated by the at least computing device, wherein the program configures the language processing model to produce statistical correlations among the plurality of first data entries;

receiving a second training set including a plurality of second data entries, each second data entry of the plurality of second data entries including at least a second prognostic label and at least a correlated third prognostic label;

record at least a first biological extraction;

a prognostic label learner operating on the at least a computing device, the prognostic label learner designed and configured to generate at least a first prognostic output as a function of the first training set and the at least a physiological test sample, wherein the prognostic label learner is further configured to generate a third prognostic output as a function of the first training set and at least a second biological extraction; and

a causal link learner operating on the at least a computing device, the causal link learner designed and configured to generate at least a second prognostic output as a function of the second training set and the at least a first prognostic output, wherein the at least a second prognostic output represents a cause of the at least a first prognostic output, and wherein the at least a second prognostic output further comprises a plurality of second prognostic outputs;

wherein the at least a computing device is further configured to determine that the at least a second prognostic output includes a fundamental prognostic label.

2. The system of claim 1 , wherein the second training set further comprises at least a data entry including at least a second element of physiological data and at least a correlated fourth prognostic label.

3. The system of claim 2 , wherein the causal link learner is further configured to generate the second prognostic output as a function of the second training set, the first prognostic output, and the at least first biological extraction.

4. The system of claim 1 , wherein the at least a computing device is configured to:

generate a third prognostic output; and

select a second prognostic output from the plurality of second prognostic outputs by determining that the second prognostic output matches the third prognostic output.

5. The system of claim 1 , wherein the computing device is further configured to generate the at least a second prognostic output by executing a K-nearest neighbors algorithm as a function of the second training set and the at least a first prognostic output.

6. The system of claim 1 , wherein the computing device is further configured to determine that the at least a second prognostic output includes a fundamental prognostic label by identifying a prognostic label of the at least a second prognostic output in a fundamental label listing.

7. The system of claim 6 , wherein the fundamental label listing includes an entry by an expert identifying a prognostic label of the at least a second prognostic output as a fundamental prognostic label.

8. The system of claim 1 , wherein the computing device is further configured to determine that the at least a second prognostic output includes a fundamental prognostic label by:

determining a number of entries in the second training set identifying a prognostic label of the at least a second prognostic output as caused by a fourth prognostic label; and

determining that the number of entries fails a threshold comparison.

9. The system of claim 1 , wherein the computing device is further configured to:

display one or more follow-up suggestions for acquisition of at least a second biological extraction at a user output device; and

receive the at least a second biological extraction.

10. The system of claim 1 wherein the at least a computing device is further configured to:

receive at least a second biological extraction;

generate a third prognostic output as a function of the first training set and the at least a second biological extraction;

determine that a single prognostic output of the plurality of second prognostic outputs contradicts the third prognostic output; and

eliminate the single prognostic output from the plurality of second prognostic outputs.

11. A method of causative chaining of prognostic label classifications, the method comprising:

receiving, by at least a computing device, training data, wherein receiving the training data further comprises:

receiving a first training set including a plurality of first data entries, each first data entry of the plurality of first data entries including at least a first element of physiological state data and at least a correlated first prognostic label;

receiving a list of significant categories of physiological state data;

associating the at least correlated first prognostic label with at least a category from the list of significant categories of prognostic labels, as a function of receiving the first training data set and the list of significant categories, utilizing a language processing model comprising a program generated by the at least computing device, wherein the program configures the language processing model to produce statistical correlations among the plurality of first data entries;

receiving a second training set including a plurality of second data entries, each second data entry of the plurality of second data entries including at least a second prognostic label and at least a correlated third prognostic label; and

recording, by the at least a computing device, at least a first biological extraction;

generating, by the computing device, at least a first prognostic output as a function of the first training set and the at least a physiological test sample;

generating, by the at least a computing device, at least a second prognostic output as a function of the second training set and the at least a first prognostic output, wherein the at least a second prognostic output represents a cause of the at least a first prognostic output; and

determining that the at least a second prognostic output includes a fundamental prognostic label.

12. The method of claim 11 , wherein the second training set further comprises at least a data entry including at least a second element of physiological data and at least a correlated fourth prognostic label.

13. The method of claim 12 , wherein generating the second prognostic output further comprises generating the second prognostic output as a function of the second training set, the first prognostic output, and the at least a biological extraction.

14. The method of claim 11 , further comprising:

generating a third prognostic output; and

selecting a second prognostic output from the plurality of second prognostic outputs by determining that the second prognostic output matches a third prognostic output.

15. The method of claim 11 , further comprising generating the at least a second prognostic output by executing a K-nearest neighbors algorithm as a function of the second training set and the at least a first prognostic output.

16. The method of claim 11 , determining that the at least a second prognostic output includes a fundamental prognostic label further comprises identifying a prognostic label of the at least a second prognostic output in a fundamental label listing.

17. The method of claim 16 , wherein the fundamental label listing includes an entry by an expert identifying a prognostic label of the at least a second prognostic output as a fundamental prognostic label.

18. The method of claim 11 , wherein determining that the at least a second prognostic output includes a fundamental prognostic label further comprises:

determining a number of entries in the second training set identifying a prognostic label of the at least a second prognostic output as caused by a fourth prognostic label; and

determining that the number of entries fails a threshold comparison.

19. The method of claim 11 further comprising:

displaying one or more follow-up suggestions for acquisition of at least a second biological extraction at a user output device; and

receiving the at least a second biological extraction.

20. The method of claim 11 further comprising:

receive at least a second biological extraction;

generating a third prognostic output as a function of the first training set and the at least a second biological extraction;

determining that a single prognostic output of the plurality of second prognostic outputs contradicts the third prognostic output; and

eliminating the single prognostic output from the plurality of second prognostic outputs.