Utilizing neural network models for recommending and adapting treatments for users

A device, comprising:

• one or more memories;
• and
• one or more processors, communicatively coupled to the one or more memories, configured to:
• receive user data identifying vitals of users when receiving treatments and dosages of the treatments,
• wherein the user data includes one of:
• historical user data identifying historical vitals of the users when
• receiving the treatments and historical dosages of the treatments, or
• simulated user data identifying simulated vitals of the users when
• receiving the treatments and simulated dosages of the treatments;
• process the user data, with a divergence model, to determine divergence data identifying divergences between the users, wherein the divergence model uses a kernel density estimation (KDE) and a Jensen-Shannon method to determine divergences between the users;
• process the divergence data, with a clustering model, to group the users into clusters of users, wherein the clustering model includes a hierarchical clustering model and comprises applying the hierarchical clustering model to the divergence data to group the users into the clusters of users;
• train a first neural network model, with the user data, to identify treatments for the clusters of users and to generate a trained first neural network model, wherein the training of the first neural network model includes:
• receiving, by the device, a set of observations;
• determining, by the device, a target variable for the set of observations;
• and
• training, by the device, the first neural network model using the target variable;
• train a second neural network model, with the user data, to determine simulated outcomes for the treatments and to generate a trained second neural network model, wherein the training of the second neural network includes:
• obtaining, by the device, additional user data;
• determining, by the device, the simulation of outcomes based on the additional user data;
• and
• causing, by the device, the second neural network model to train, based on the simulated outcome;
• generate a treatment model based on the trained first neural network model and the trained second neural network model;
• train the treatment model with the additional user data to generate a trained treatment model;
• process new user data identifying a new user, with the trained treatment model, to determine a recommended treatment for the new user;
• and
• perform one or more actions based on the recommended treatment for the new user.

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• receive user data identifying vitals of users when receiving treatments and dosages of the treatments;
• process the user data, with a divergence model, to determine divergence data identifying divergences between the users, wherein the divergence model uses a kernel density estimation (KDE) and a Jensen-Shannon method to determine divergences between the users;
• process the divergence data, with a clustering model, to group the users into clusters of users, wherein the clustering model includes a hierarchical clustering model and comprises applying the hierarchical clustering model to the divergence data to group the users into the clusters of users;
• train a first neural network model, with the user data, to identify treatments for the clusters of users and to generate a trained first neural network model, wherein the training of the first neural network model includes:
• receiving, by the device, a set of observations;
• determining, by the device, a target variable for the set of observations;
• and
• training, by the device, the first neural network model using the target variable;
• train a second neural network model, with the user data, to determine simulated outcomes for the treatments and to generate a trained second neural network model, wherein the training of the second neural network model includes:
• obtaining, by the device, additional user data;
• determining, by the device, the simulation of outcomes based on the additional user data;
• and
• causing, by the device, the second neural network model to train, based on the simulated outcome;
• generate a treatment model based on the trained first neural network model and the trained second neural network model;
• train the treatment model with the additional user data to generate a trained treatment model;
• process new user data identifying a new user, with the divergence model, to determine new divergence data identifying a divergence between the clusters of users and the new user;
• process the new divergence data, with the clustering model, to assign the new user to one of the clusters of users;
• process the new user data, with the trained treatment model, to determine a recommended treatment for the new user based on the one of the clusters of users assigned to the new user;
• and
• perform one or more actions based on the recommended treatment for the new user.