Methods and systems for physiologically informed therapeutic provisions

1. A system for physiologically informed therapeutic provisions, the system comprising a computing device, the computing device designed and configured to:

receive, from a remote device operated by a user:

a conditional datum, wherein the conditional datum contains a description of a current bodily complaint; and

a factor, wherein the factor describes at least a budgetary constraint of the user for a particular antidotal therapeutic provision;

identify a plurality of antidotal therapeutic provisions, using a therapeutic clustering model, wherein the therapeutic clustering model utilizes the conditional datum as an input and outputs the plurality of antidotal therapeutic provisions;

locate a first user biological extraction, wherein the first user biological extraction contains at least an element of user physiological data;

train, using compatibility training data and a machine-learning algorithm, a compatibility machine-learning model, wherein the compatibility training data comprises antidotal therapeutic data and biological extraction data correlated with compatible antidotal therapeutic data; and

generate, using the trained compatibility machine-learning model, at least a compatible antidotal therapeutic provision, wherein the plurality of antidotal therapeutic provisions, the factor and the first user biological extraction are provided to the trained compatibility machine-learning model as inputs to output the at least a compatible antidotal therapeutic provision.

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

retrieve a second user biological extraction;

receive conditional training data, wherein the conditional training data contains a plurality of biological extractions and a plurality of correlated conditions; and

generate a conditional model, wherein the conditional model utilizes the second user biological extraction as an input and outputs the conditional datum containing a suspected condition.

3. The system of claim 1 , wherein the computing device is further configured to receive from the remote device, operated by an informed advisor, the conditional datum containing a current bodily diagnosis.

4. The system of claim 1 , wherein the computing device is further configured to identify the plurality of antidotal therapeutic provisions using the therapeutic clustering model by:

receiving a clustering dataset, wherein the clustering dataset further comprises a plurality of unclassified cluster data entries; and

calculating a first clustering algorithm.

5. The system of claim 4 , wherein the first clustering algorithm further comprises a k-means clustering algorithm.

6. The system of claim 4 , wherein the first clustering algorithm further comprises a hierarchical clustering algorithm.

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

calculate a user effective age utilizing a user chronological age and a third user biological extraction; and

analyze output antidotal therapeutic provisions as a function of the user effective age.

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

input the conditional datum to a biological classifier, the biological classifier configured to input conditional datums and output related biological extractions by executing a classification algorithm;

locate a fourth user biological extraction related to the conditional datum; and

generate the compatibility model utilizing the fourth user biological extraction related to the conditional datum.

9. The system of claim 1 , wherein the system further comprises a sensor configured to detect the at least an element of user physiological data.

10. A method of physiologically informed therapeutic provisions, the method comprising:

receiving, by a computing device, from a remote device operated by a user:

a conditional datum, wherein the conditional datum contains a description of a current bodily complaint; and

a factor, wherein the factor describes at least a budgetary constraint of the user for a particular antidotal therapeutic provision;

identifying, by the computing device, a plurality of antidotal therapeutic provisions, using a therapeutic clustering model, wherein the therapeutic clustering model utilizes the conditional datum as an input and outputs the plurality of antidotal therapeutic provisions

locating, by the computing device, a first user biological extraction, wherein the first user biological extraction contains at least an element of user physiological data;

training, by the computing device, using compatibility training data and a machine-learning algorithm, a compatibility machine-learning model, wherein the compatibility training data comprises antidotal therapeutic data and biological extraction data correlated with compatible antidotal therapeutic data; and

generating, by the computing device, using the trained compatibility machine-learning model, at least a compatible antidotal therapeutic provision, wherein the plurality of antidotal therapeutic provisions, the factor and the first user biological extraction are provided to the trained compatibility machine-learning model as inputs to output the at least a compatible antidotal therapeutic provision.

11. The method of claim 10 , wherein receiving the conditional datum further comprises:

retrieving a second user biological extraction;

receiving, conditional training data, wherein the conditional training data contains a plurality of biological extractions and a plurality of correlated conditions; and

generating a conditional model, wherein the conditional model utilizes the second user biological extraction as an input and outputs the conditional datum containing a suspected condition.

12. The method of claim 10 , wherein receiving the conditional datum further comprises receiving from the remote device, operated by an informed advisor, the conditional datum containing a current bodily diagnosis.

13. The method of claim 10 , wherein identifying the plurality of antidotal therapeutic provisions further comprises:

receiving a clustering dataset, wherein the clustering dataset further comprises a plurality of unclassified cluster data entries; and

calculating a first clustering algorithm.

14. The method of claim 13 , wherein calculating the first clustering algorithm further comprises calculating a k-means clustering algorithm.

15. The method of claim 13 , wherein calculating the first clustering algorithm further comprises calculating a hierarchical clustering algorithm.

16. The method of claim 10 , wherein identifying the plurality of antidotal therapeutic provisions further comprises:

calculating a user effective age utilizing a user chronological age and a third user biological extraction; and

analyzing output antidotal therapeutic provisions as a function of the user effective age.

17. The method of claim 10 , wherein locating the biological extraction further comprises:

inputting the conditional datum to a biological classifier, the biological classifier configured to input conditional datums and output related biological extractions by executing a classification algorithm;

locating a fourth user biological extraction related to the conditional datum; and

generating the compatibility model utilizing the fourth user biological extraction related to the conditional datum.

18. The method of claim 10 , wherein the method further comprises detecting, by a sensor, the at least an element of user physiological data.