Computer network architecture for a pipeline of models for healthcare outcomes with machine learning and artificial intelligence

1. A computer network architecture with automated model pipeline artificial intelligence, and machine learning, comprising:

a prediction module with a prediction generator and a prediction database, and

a learning module with a learning database and a training database, the learning database including a predictive model library storing calibrated algorithms and associated evaluation metrics, and the training database including a model type library storing algorithms for a plurality of model types, and

a web application in electronic communication with both the prediction module and a user device;

wherein, the learning module is configured to:

receive a list of algorithm definitions and a dataset,

calibrate one or more algorithms for a plurality of the model types with the dataset,

test the calibrated algorithms with a plurality of evaluation metrics,

store the calibrated algorithms and evaluation metrics in the predictive model library,

select an algorithm from the calibrated algorithms based on the evaluation metrics and store the selected algorithms in the prediction database,

update the training database with third party data, and user episode data, and

re-execute the calibrate, test, store, select, and update steps automatically and continually, either periodically, occasionally, or with each user prediction request;

wherein, the prediction generator is configured to:

receive the user prediction request, including episode data and a client model,

update the training database with the episode data in the user prediction request,

run the currently selected algorithm corresponding to the user using the episode data, and generate prediction output,

generate a prediction report based on the algorithm output, and

transmit the prediction report to the user;

wherein the specified prediction output includes a plurality of the specified healthcare outcomes with at least one output for each of: patient healthcare outcomes, costs, quality of care, patient satisfaction, insurance claims, and clinical outcomes;

wherein, the model types include at least one of each of:

multi-level models,

random forest regression,

logistical regression,

gamma-distributed regression, and

linear regression;

wherein, the third-party data is from a plurality of parties, with at least one party of each type in the group of types comprising:

hospitals,

medical practices,

insurance companies,

credit reporting agencies, and

credit rating agencies;

wherein, the training database includes patient medical data, patient personal data, patient outcome data, and medical treatment data;

wherein, the episode data includes individual patient medical data and personal data; and

wherein, the user is a member of the group comprising:

hospitals

medical practices, and

insurance companies;

wherein, the user device is remote from the prediction module, and the user device is a member of the group comprising:

a computer,

a desktop PC,

a laptop PC,

a smart phone,

a tablet computer, and

a personal wearable computing device;

wherein, the web application communicates with the user device by the Internet, or an extranet, or a VPN, or other network, and the web application is generic for any users, or customized for a specific user, or class of user, and

wherein, the web application is specific to a specific class of user requests, and

wherein, the prediction generator is further configured to:

receive a user prediction request for specified healthcare outcomes, including episode data and a client model,

run the currently selected algorithm corresponding to the user using the episode data, and generate prediction output for the specified healthcare outcomes,

generate a prediction report for the specified healthcare outcomes based on the algorithm output, and

transmit the prediction report to the user.

2. A computer network architecture with automated model pipeline artificial intelligence, and machine learning, comprising:

a prediction module with a prediction generator and a prediction database, and

a learning module with a learning database and a training database, the learning database including a predictive model library storing calibrated algorithms and associated evaluation metrics, and the training database including a model type library storing algorithms for a plurality of model types,

wherein, the learning module is configured to:

receive a list of algorithm definitions and a dataset,

calibrate one or more algorithms for a plurality of the model types with the dataset,

test the calibrated algorithms with a plurality of evaluation metrics,

store the calibrated algorithms and evaluation metrics in the predictive model library,

select an algorithm from the calibrated algorithms based on the evaluation metrics and store the selected algorithm in the prediction database,

update the training database with third party data, and user episode data, and

re-execute the calibrate, test, store, select, and update steps automatically and continually, either periodically, occasionally, or with each user prediction request;

wherein, the prediction generator is configured to:

receive the user prediction request, including episode data and a client model,

update the training database with the episode data in the user prediction request,

run the currently selected algorithm corresponding to the user using the episode data, and generate prediction output for the specified healthcare outcomes,

generate a prediction report for the specified healthcare outcomes based on the algorithm output, and

transmit the prediction report to the user.

3. The computer network architecture of claim 2 , wherein the specified prediction output includes a plurality of the specified healthcare outcomes with at least one output for each of: patient healthcare outcomes, costs, quality of care, patient satisfaction, insurance claims, and clinical outcomes.

4. The computer network architecture of claim 2 , wherein: the model types include at least one of each of:

multi-level models,

random forest regression,

logistical regression,

gamma-distributed regression, and

linear regression;

the third-party data is from a plurality of parties, with at least one party of each type in the group of types comprising:

hospitals,

medical practices,

insurance companies,

credit reporting agencies, and

credit rating agencies;

the training database includes patient medical data, patient personal data, patient outcome data, and medical treatment data;

the episode data includes individual patient medical data and personal data; and

the user is a member of the group comprising:

hospitals

medical practices, and

insurance companies.

5. The computer network architecture of claim 2 , further comprising:

a web application in electronic communication with both the prediction module, and a user device.

6. The computer network architecture of claim 5 , wherein the user device is remote from the prediction module, and

the user device is a member of the group comprising:

a computer,

a desktop PC,

a laptop PC,

a smart phone,

a tablet computer, and

a personal wearable computing device.

7. The computer network architecture of claim 5 , wherein the web application communicates with the user device by the Internet, or an extranet, or a VPN, or other network, and the web application is generic for any users, or customized for a specific user, or class of user, and

wherein, the web application is specific to a specific class of user requests.