SYSTEMS AND METHODS FOR GENERATING A PARASITIC INFECTION PROGRAM
A system and method for generating a parasitic infection program is disclosed. The system includes a computing device configured to receive parasitic background training data including a plurality of parasitic biomarkers as input correlated to a plurality of parasitic disease as output, train a parasitic background machine-learning model with the parasitic background training data, generate a parasitic background as a function of the parasitic background machine-learning model and at least a parasitic biomarker including a host factor, generate a parasitic disease assessment as a function of the parasitic background, which includes classifying the parasitic background into the parasitic disease assessment using an assessment classification machine-learning process and generate a parasitic infection program as a function of the parasitic disease assessment using a parasitic program model trained with parasitic program training data including a plurality of parasitic disease assessments as input correlated to a plurality of parasitic infection programs as output.
1 . A system for generating a parasitic infection program, the system comprising:
a computing device, wherein the computing device is configured to:
receive parasitic background training data, wherein the parasitic background training data comprises a plurality of parasitic biomarkers as input correlated to a plurality of parasitic disease as output;
train a parasitic background machine-learning model with the parasitic background training data;
generate a parasitic background as a function of the parasitic background machine-learning model and at least a parasitic biomarker, wherein the at least a parasitic biomarker comprises a host factor;
generate a parasitic disease assessment as a function of the parasitic background, wherein generating the parasitic disease assessment comprises classifying the parasitic background into the parasitic disease assessment using an assessment classification machine-learning process; and
generate a parasitic infection program as a function of the parasitic disease assessment using a parasitic program model, wherein the parasitic program model is trained with parasitic program training data comprising a plurality of parasitic disease assessments as input correlated to a plurality of parasitic infection programs as output.
2 . The system of claim 1 , wherein the at least a parasitic biomarker comprises a change of parasite as a function of immune response of a host body.
3 . The system of claim 1 , wherein:
the parasitic disease assessment comprises an organ type classification; and
the computing device is further configured to generate the parasitic infection program as a function of the organ type classification.
4 . The system of claim 1 , wherein:
the parasitic disease assessment comprises a prospective medical condition predictive classification; and
the computing device is further configured to generate the parasitic infection program as a function of the prospective medical condition predictive classification.
5 . The system of claim 1 , wherein:
the parasitic disease assessment comprises a duration classification, wherein the duration classification comprises an acute disease; and
the computing device is further configured to generate the parasitic infection program as a function of the duration classification.
6 . The system of claim 1 , wherein the parasitic infection program comprises a parasitic infection nutrition program, wherein the parasitic infection nutrition program comprises a frequency of at least a nutrition element.
7 . The system of claim 6 , wherein the computing device is further configured to determine the at least a nutrition element, wherein determining the at least a nutrition element comprises:
training a nutrition model using a nutrition machine-learning process and training data, wherein training data includes a plurality of data entries of nutrition amounts correlated to nutrition elements; and
determining the at least a nutrition element as a function of the at least a nutrient amount using the nutrition model.
8 . The system of claim 1 , wherein the computing device is further configured to calculate a parasitic infection relapse rate as a function of the parasitic infection program and the host factor of the at least a parasitic biomarker, wherein calculating the parasitic infection relapse rate comprises:
training a relapse rate model using a relapse machine-learning process and relapse rate training data, wherein the relapse rate training data comprises a plurality of parasitic infection programs and host factors as input and a plurality of parasitic infection relapse rates as output; and
determining the parasitic infection relapse rate as a function of the parasitic infection program and the host factor using the trained relapse rate model.
9 . The system of claim 1 , wherein the computing device is further configured to:
calculate a program resistance rate as a function of the parasitic infection program and the at least a parasitic biomarker;
generate a second parasitic infection program as a function of the program resistance rate.
10 . The system of claim 9 , wherein calculating the program resistance rate comprises:
training a resistance rate model using a resistance machine-learning process and resistance rate training data, wherein the resistance rate training data comprises a plurality of parasitic infection programs and parasitic biomarkers as input correlated to a plurality of program resistance rates as output; and
determining the program resistance rate as a function of the parasitic infection program and the at least a parasitic biomarkers using the trained resistance machine-learning process.
11 . A method for generating a parasitic infection program, the method comprising:
receiving, using a computing device, parasitic background training data, wherein the parasitic background training data comprises a plurality of parasitic biomarkers as input correlated to a plurality of parasitic disease as output;
training, using the computing device, a parasitic background machine-learning model with the parasitic background training data;
generating, using the computing device, a parasitic background as a function of the parasitic background machine-learning model and at least a parasitic biomarker, wherein the at least a parasitic biomarker comprises a host factor;
generating, using the computing device, a parasitic disease assessment as a function of the parasitic background, wherein generating the parasitic disease assessment comprises classifying the parasitic background into the parasitic disease assessment using an assessment classification machine-learning process; and
generating, using the computing device, a parasitic infection program as a function of the parasitic disease assessment using a parasitic program model, wherein the parasitic program model is trained with parasitic program training data comprising a plurality of parasitic disease assessments as input correlated to a plurality of parasitic infection programs as output.
12 . The method of claim 11 , wherein the at least a parasitic biomarker comprises a change of parasite as a function of immune response of a host body.
13 . The method of claim 11 , further comprising:
generating, using the computing device, the parasitic infection program as a function of an organ type classification of the parasitic disease assessment.
14 . The method of claim 11 , further comprising:
generating, using the computing device, the parasitic infection program as a function of a prospective medical condition predictive classification of the parasitic disease assessment.
15 . The method of claim 11 , further comprising:
generating, using the computing device, the parasitic infection program as a function of a duration classification of the parasitic disease assessment, wherein the duration classification comprises an acute disease.
16 . The method of claim 11 , wherein the parasitic infection program comprises a parasitic infection nutrition program, wherein the parasitic infection nutrition program comprises a frequency of at least a nutrition element.
17 . The method of claim 16 , further comprising:
determining, using the computing device, the at least a nutrition element, wherein determining the at least a nutrition element comprises:
training a nutrition model using a nutrition machine-learning process and training data, wherein training data includes a plurality of data entries of nutrition amounts correlated to nutrition elements; and
determining the at least a nutrition element as a function of the at least a nutrient amount using the nutrition model.
18 . The method of claim 11 , further comprising:
calculating, using the computing device, a parasitic infection relapse rate as a function of the parasitic infection program and the host factor of the at least a parasitic biomarker, wherein calculating the parasitic infection relapse rate comprises:
training a relapse rate model using a relapse machine-learning process and relapse rate training data, wherein the relapse rate training data comprises a plurality of parasitic infection programs and host factors as input and a plurality of parasitic infection relapse rates as output; and
determining the parasitic infection relapse rate as a function of the parasitic infection program and the host factor using the trained relapse rate model.
19 . The method of claim 11 , further comprising:
calculating, using the computing device, a program resistance rate as a function of the parasitic infection program and the at least a parasitic biomarker; and
generating, using the computing device, a second parasitic infection program as a function of the program resistance rate.
20 . The method of claim 19 , further comprising:
training, using the computing device, a resistance rate model using a resistance machine-learning process and resistance rate training data, wherein the resistance rate training data comprises a plurality of parasitic infection programs and parasitic biomarkers as input correlated to a plurality of program resistance rates as output; and
determining, using the computing device, the program resistance rate as a function of the parasitic infection program and the at least a parasitic biomarkers using the trained resistance machine-learning process.