Methods and systems for optimizing supplement decisions
A system for optimizing supplement decisions is disclosed. The system includes a computing device configured to receive a longevity inquiry from a remote device. The system retrieves a biological extraction pertaining to a user and identifies a longevity element associated with a user. The system selects an ADME model utilizing a biological extraction. The system generates a machine-learning algorithm utilizing the selected ADME model to input a longevity element associated with a user as an input and output an ADME factor. The system identifies a second longevity element compatible with the ADME factor as a function of the first longevity element. The system selects the second longevity element as a tolerant longevity element. A method for optimizing supplement decisions is also disclosed.
1. A system for optimizing supplement decisions, the system comprising a computing device, the computing device further configured to:
receive a longevity inquiry from a remote device;
retrieve a first biological extraction from a user database, wherein the first biological extraction comprises a gut-wall body measurement related to a stool test result measuring a presence of a disease state;
identify a first longevity element associated with a user as a function of the longevity inquiry and the first biological extraction;
correlate a genetic marker from the first biological extraction with an ADME (Absorption, Distribution, Metabolism, and Excretion) model using a genetic classifier, wherein the genetic classifier is trained using training data, wherein the training data is updated based on at least a previously selected ADME model, such that the genetic classifier is customized to the user, wherein the genetic marker comprises a molecular marker configured to detect a variation of a nucleotide change, and wherein the nucleotide change comprises deletion, duplication, inversion, or insertion;
select the ADME model as a function of the first biological extraction and the genetic marker;
generate a machine-learning algorithm utilizing the selected ADME model that inputs the longevity element associated with the user as an input and outputs an ADME factor, wherein the ADME factor includes an absorption rate of the longevity element and the ADME factor is utilized by the machine-learning algorithm to compare longevity elements and select the longevity element the user is most tolerant of;
identify a second longevity element compatible with the ADME factor as a function of the first longevity element; and
select the second longevity element as a tolerant longevity element.
2. The system of claim 1 , wherein receiving the longevity inquiry from a remote device further comprises receiving at an image device located on the computing device a wireless transmission from the remote device containing a photograph of a longevity element.
3. The system of claim 1 , wherein identifying the first longevity element associated with the user further comprises:
receiving dietary training data wherein dietary training data includes a plurality of biological extractions and a plurality of correlated longevity elements;
generating using a first machine learning algorithm a dietary model relating biological extractions to longevity elements;
receiving a second biological extraction; and
outputting the first longevity element using the first machine learning algorithm and the second biological extraction.
4. The system of claim 3 , wherein the first machine learning algorithm further comprises a supervised machine-learning algorithm.
5. The system of claim 1 , wherein selecting an ADME model further comprises:
identifying a genetic marker contained within a third biological extraction;
generating using genetic training data including a plurality of genetic markers and a plurality of correlated ADME models, and using a classification algorithm, a genetic classifier, wherein the genetic classifier inputs a genetic marker and outputs an ADME model; and
selecting an ADME model as a function of generating the genetic classifier.
6. The system of claim 1 , wherein selecting the ADME model further comprises:
retrieving a fourth biological extraction from the user database wherein the fourth biological extraction further comprises a genetic marker containing an ADME marker; and
locating an ADME model containing the ADME marker.
7. The system of claim 1 , wherein the computing device is further configured to identify the second longevity element compatible with the ADME factor as a function of the first longevity element by:
identifying a first active ingredient contained in the first tolerant longevity element; and
identifying a second longevity element containing a second active ingredient, wherein the first active ingredient relates to the second active ingredient.
8. The system of claim 7 , wherein the computing device is further configured to eliminate the first longevity element.
9. The system of claim 1 , wherein the computing device is further configured to select the second longevity element as a tolerant longevity element by:
identifying a second longevity element compatible with the ADME factor; and
selecting the second longevity element as a tolerant longevity element as a function of the identification.
10. The system of claim 1 , wherein the computing device is further configured to display the second longevity element to the user.
11. A method of optimizing supplement decisions, the method comprising:
receiving by a computing device a longevity inquiry from a remote device;
retrieving by the computing device a first biological extraction from a user database, wherein the first biological extraction comprises a gut-wall body measurement related to a stool test result measuring a presence of a disease state;
identifying by the computing device a longevity element associated with a user as a function of the longevity inquiry and the first biological extraction;
correlating a genetic marker from the first biological extraction with an ADME (Absorption, Distribution, Metabolism, and Excretion) model using a genetic classifier, wherein the genetic classifier is trained using training data, wherein the training data is updated based on at least a previously selected ADME model, such that the genetic classifier is customized to the user, wherein the genetic marker comprises a molecular marker configured to detect a variation of a nucleotide change, and wherein the nucleotide change comprises deletion, duplication, inversion, or insertion;
selecting by the computing device the ADME model as a function of the first biological extraction and the genetic marker;
generating, by the computing device, a machine-learning algorithm utilizing the selected ADME model that inputs the longevity element associated with the user as an input and outputs an ADME factor, wherein the ADME factor includes an absorption rate of the longevity element and the ADME factor is utilized by the machine-learning algorithm to compare longevity elements and select the longevity element the user is most tolerant of;
identifying a second longevity element compatible with the ADME factor; and
selecting the second longevity element as a tolerant longevity element.
12. The method of claim 11 , wherein receiving the longevity inquiry from a remote device further comprises receiving at an image device located on the computing device a wireless transmission from the remote device containing a photograph of a longevity element.
13. The method of claim 11 , wherein identifying the first longevity element associated with the user further comprises:
receiving dietary training data wherein dietary training data includes a plurality of biological extractions and a plurality of correlated longevity elements;
generating using a first machine learning algorithm a dietary model correlating biological extractions with longevity elements;
receiving a second biological extraction; and
outputting the first longevity element using the first machine learning algorithm.
14. The method of claim 13 , wherein the first machine learning algorithm further comprises a supervised machine-learning algorithm.
15. The method of claim 11 , wherein selecting the ADME model further comprises:
identifying a genetic marker contained within a third biological extraction;
generating using genetic training data including a plurality of genetic markers and a plurality of correlated ADME models, and using a classification algorithm, a genetic classifier, wherein the genetic classifier inputs a genetic marker and outputs an ADME model; and
selecting an ADME model as a function of generating the genetic classifier.
16. The method of claim 11 , wherein selecting the ADME model further comprises:
retrieving a fourth biological extraction from the user database wherein the biological extraction further comprises a genetic marker containing an ADME marker; and
locating an ADME model containing the ADME marker.
17. The method of claim 11 , wherein identifying a second longevity element compatible with the ADME factor as a function of the first longevity element further comprises:
identifying a first active ingredient contained in the first tolerant longevity element; and
identifying a second longevity element containing a second active ingredient, wherein the first active ingredient relates to the second active ingredient.
18. The method of claim 17 , wherein the method further comprises eliminating the first longevity element.
19. The method of claim 11 , further comprising selecting a tolerant longevity element by:
identifying a second longevity element compatible with the ADME factor; and
selecting the second longevity element as a tolerant longevity element.
20. The method of claim 19 , further comprising displaying the second longevity element to the user.