METHODS AND SYSTEMS FOR CUSTOMIZING TREATMENTS

1 . A system for customizing treatments, the system comprising:

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

calculate a condition state label as a function of an element of user physiological data;

generate a treatment model, using a second machine-learning algorithm and a treatment training set, wherein the treatment model utilizes condition state labels as inputs and outputs treatments, wherein generating the treatment model further comprises:

calculating a treatment category selector as a function of an implementation factor, wherein the implementation factor indicates a user preference pertaining to different treatment practices;

output a treatment utilizing the treatment model to a remote device;

receive a treatment response from a remote device; and

generate a treatment response score as a function of the treatment response.

2 . The system of claim 1 , wherein the computing device is further configured to calculate a current condition state progression indicator, wherein calculating the current condition state progression indicator comprises generating a progression model, wherein the progression model comprises a second machine-learning model trained by progression training data comprising a plurality of physiological data sets and a plurality of correlated progression indicators and wherein the progression model is configured to receive the element of user physiological data as an input and output a current condition state progression indicator.

3 . The system of claim 1 , wherein:

the computing device is further configured to categorize the treatment response to a treatment response category; and

generating the treatment response score as a function of the treatment response comprises generating the treatment response score as a function of treatment response and the treatment response category.

4 . The system of claim 3 , wherein categorizing the treatment response to the treatment response category comprises:

receiving treatment response training data, wherein the treatment response training data comprises a plurality of treatment responses correlated to treatment response categories;

training a treatment response classifier using the treatment response training data; and

categorizing the treatment response to the treatment response category using the trained treatment response classifier.

5 . The system of claim 1 , wherein the computing device is further configured to generate an encouragement notification as a function of comparing the treatment response score to a treatment response score threshold.

6 . The system of claim 5 , wherein generating the encouragement notification as a function of comparing the treatment response score to the treatment response score threshold comprises generating the encouragement notification using a large language model (LLM).

7 . The system of claim 1 , wherein the computing device is further configured to generate a healthcare notification, wherein generating the healthcare notification comprises:

selecting the healthcare professional as a function of the treatment category selector; and

inserting contact information associated with the healthcare professional into the healthcare notification.

8 . The system of claim 1 , wherein receiving the treatment response from the remote device comprises:

generating a treatment response form and transmitting the treatment response form to the remote device on a set periodic basis;

receiving a completed treatment response form from the remote device; and

extracting the treatment response from the completed treatment response form.

9 . The system of claim 1 , wherein calculating the treatment category selector further comprises multiplying an approach factor by the implementation factor and a corrective factor.

10 . The system of claim 1 , wherein generating the treatment response score as a function of the treatment response comprises:

receiving score training data, wherein the score training data comprises treatment responses correlated to treatment response scores;

training a score machine-learning model using the score training data; and

generating the treatment response score using the trained score machine-learning model.

11 . A method for customizing treatments, the method comprising:

calculating, by the computing device, a condition state label as a function of an element of user physiological data;

generating, by the computing device, a treatment model, using a second machine-learning algorithm and a treatment training set, wherein the treatment model utilizes condition state labels as inputs and outputs treatments, wherein generating the treatment model further comprises:

calculating a treatment category selector as a function of an implementation factor, wherein the implementation factor indicates a user preference pertaining to different treatment practices;

outputting, by the computing device, a treatment utilizing the treatment model to a remote device;

receiving, by the computing device, a treatment response from a remote device; and

generating, by the computing device, a treatment response score as a function of the treatment response.

12 . The method of claim 1 , further comprising calculating a current condition state progression indicator, wherein calculating the current condition state progression indicator comprises generating a progression model, wherein the progression model comprises a second machine-learning model trained by progression training data comprising a plurality of physiological data sets and a plurality of correlated progression indicators and wherein the progression model is configured to receive the element of user physiological data as an input and output a current condition state progression indicator.

13 . The method of claim 11 , wherein:

the method further comprises categorizing, by the computing device, the treatment response to a treatment response category; and

generating the treatment response score as a function of the treatment response comprises generating the treatment response score as a function of treatment response and the treatment response category.

14 . The method of claim 13 , wherein categorizing the treatment response to the treatment response category comprises:

receiving treatment response training data, wherein the treatment response training data comprises a plurality of treatment responses correlated to treatment response categories;

training a treatment response classifier using the treatment response training data; and

categorizing the treatment response to the treatment response category using the trained treatment response classifier.

15 . The method of claim 11 , further comprising generating, by the computing device, an encouragement notification as a function of comparing the treatment response score to a treatment response score threshold.

16 . The method of claim 15 , wherein generating the encouragement notification as a function of comparing the treatment response score to the treatment response score threshold comprises generating the encouragement notification using a large language model (LLM).

17 . The method of claim 11 , further comprising generating a healthcare notification, wherein generating the healthcare notification comprises:

selecting the healthcare professional as a function of the treatment category selector; and

inserting contact information associated with the healthcare professional into the healthcare notification.

18 . The method of claim 11 , wherein receiving the treatment response from the remote device comprises:

generating a treatment response form and transmitting the treatment response form to the remote device on a set periodic basis;

receiving a completed treatment response form from the remote device; and

extracting the treatment response from the completed treatment response form.

19 . The method of claim 11 , wherein calculating the treatment category selector further comprises multiplying an approach factor by the implementation factor and a corrective factor.

20 . The method of claim 11 , wherein generating the treatment response score as a function of the treatment response comprises:

receiving score training data, wherein the score training data comprises treatment responses correlated to treatment response scores;

training a score machine-learning model using the score training data; and

generating the treatment response score using the trained score machine-learning model.