METHODS AND SYSTEMS FOR REVERSING A GERIATRIC PROCESS
A system for reversing a geriatric process, the system including a computing device configured to receive, a geriatric process relating to a user; retrieve a user effective age; assign the geriatric process a geriatric group label as a function of the user effective age; identify a measurement relating to the geriatric group label; and locate a nutrient intended to address the measurement, wherein locating the nutrient includes training a machine learning process as a function of a training set, wherein the training set relates a plurality of measurements to a plurality of nutrients; and locating the nutrient as a function of the measurement and the machine learning process.
1 . A system for reversing a geriatric process, the system comprising:
a computing device the computing device designed and configured to:
receive, a geriatric process relating to a user;
retrieve a user effective age;
assign the geriatric process a geriatric group label as a function of the user effective age;
identify a measurement relating to the geriatric group label; and
locate a nutrient intended to address the measurement, wherein locating the nutrient further comprises:
training a machine learning process as a function of a training set, wherein the training set relates a plurality of measurements to a plurality of nutrients; and
locating the nutrient as a function of the measurement and the machine learning process.
2 . The system of claim 1 , wherein the computing device is further configured to:
subtract the user effective age from a user chronological age and output a deficit; and
assign the geriatric group label as a function of the deficit.
3 . The system of claim 1 , wherein the geriatric group label identifies a body system.
4 . The system of claim 1 , wherein assigning the geriatric group label further comprises:
locating a malady state associated with the user;
comparing the malady state to the geriatric process; and
assigning the geriatric group label as a function of the comparison.
5 . The system of claim 1 , wherein identifying the measurement further comprises:
generating a geriatric classifier wherein the geriatric classifier is trained using a training set that relates a plurality of geriatric processes to a plurality of measurements and the geriatric classifier is configured to input the geriatric process relating to the user and output the measurement as a function of a classification process.
6 . The system of claim 1 , wherein the measurement identifies a deficiency.
7 . The system of claim 1 , wherein the measurement identifies an excess.
8 . The system of claim 1 further comprising:
generate a remedy wherein generating the remedy further comprises:
training a machine learning process wherein the machine learning process utilizes the nutrient and the measurement as an input, and outputs the remedy.
9 . The system of claim 9 , wherein generating the remedy further comprises:
locating a plurality of remedies relating to the nutrient and the geriatric process; and
selecting a first remedy as a function of the user effective age.
10 . The system of claim 1 further comprising determining a nutrient interval.
11 . A method of reversing a geriatric process, the method comprising:
receiving by a computing device, a geriatric process relating to a user;
retrieving by the computing device, a user effective age;
assigning the geriatric process, by the computing device, a geriatric group label as a function of the user effective age;
identifying by the computing device, a measurement relating to the geriatric group label; and
locating by the computing device, a nutrient intended to address the measurement, wherein
locating the nutrient further comprises:
training a machine learning process as a function of a training set, wherein the training set relates a plurality of measurements to a plurality of nutrients; and
locating the nutrient as a function of the measurement and the machine learning process.
12 . The method of claim 11 , wherein assigning the geriatric group label further comprises:
subtracting the user effective age from a user chronological age and output a deficit; and
assigning the geriatric group label as a function of the deficit.
13 . The method of claim 11 , wherein the geriatric group label identifies a body system.
14 . The method of claim 11 , wherein assigning the geriatric group label further comprises:
locating a malady state associated with the user;
comparing the malady state to the geriatric process; and
assigning the geriatric group label as a function of the comparison.
15 . The method of claim 11 , wherein identifying the measurement further comprises:
generating a geriatric classifier wherein the geriatric classifier is trained using a training set that relates a plurality of geriatric processes to a plurality of measurements and the geriatric classifier is configured to input the geriatric process relating to the user and output the measurement as a function of a classification process.
16 . The method of claim 11 , wherein the measurement identifies a deficiency.
17 . The method of claim 11 , wherein the measurement identifies an excess.
18 . The method of claim 11 further comprising:
generating a remedy wherein generating the remedy further comprises:
training a machine learning process wherein the machine learning process utilizes the nutrient and the measurement as an input, and outputs the remedy.
19 . The method of claim 19 , wherein generating the remedy further comprises:
locating a plurality of remedies relating to the nutrient and the geriatric process; and
selecting a first remedy as a function of the user effective age.
20 . The method of claim 11 further comprising determining a nutrient interval.