Method and system of generating, delivering and displaying cross platform personalized digital software applications

1 . A computerized method for generating a personalized digital software application comprising:

with an application design system running on a specific operating system:

providing an application modeler engine;

with the application modeler engine:

providing a graphical display of a palate that comprises a list all the nodes that are available to include in an application definition file,

receiving a set of nodes via a drag and drop operation into the application definition file, wherein the set of nodes are wired together by the drag and drop operation to generate a graph that is saved as the application definition file, and wherein each node comprises a plurality customizable parameters to tailor an information for an application,

defining and integrating a chatbot into the personalized digital software application, wherein the chatbot receives a query from the user, wherein the chatbot comprises a natural language processing engine that is configured to analyze the query, and replies with a multi-media answer to the mobile device, wherein the multi-media answer comprises an image-based information for the query or a video-based information for the query to be displayed on the mobile device, automatically creating the application definition file to run on the mobile device of a user, wherein the application definition file follows a protocol and logic created in the application by a care team and the drag and dropped nodes; and

with a machine-learning engine:

generating a plurality of machine learning models configured to generate and optimize the application definition file for the user, wherein one or more deep machine learning methods are configured to generate and validate the user and use the user's input to create the application as a personalized digital software application, wherein the one or more deep machine learning methods comprise:

a machine learning pipeline configured to analyze user data to determine risk stratification,

wherein the deep machine learning methods implement statistical regression on the data from users to determine temporal patterns,

wherein regression slopes for clinical measurements are calculated and compared to predefined thresholds to determine user status,

wherein if a slope of a clinical measurement exceeds a first threshold then a user status is assigned a first risk level, and if the slope exceeds a second higher threshold then the user status is assigned a second higher risk level,

wherein the plurality of machine learning models comprise:

a first machine learning model trained to process user-specific data from the set of nodes,

a second machine learning model configured to optimize the application definition file based on the user inputs,

a third machine learning model configured to infer answers and components for the application, and

wherein the plurality of machine learning models are trained using a dataset comprising measurements from a peripheral, the user input via a status variable, an answer to a question, and a medical record;

with the plurality of machine learning models with the application definition file as an input, generating an optimized application definition file for the user,

with the optimized application definition file, generating the application from the optimized application definition file, wherein the plurality of machine learning models are configured to use machine learning to infer a plurality of answers and components of the application,

deploying the application to a user's mobile device, and

receiving a set of user inputs into the set of nodes;

based on the user's inputs into the set of nodes, optimize a risk score assignment to the user; and

displaying the image-based information for the query or the video-based information for the query that is displayed on the mobile device;

providing the application deployed to the user's mobile device, wherein each node in the application definition file is executed in a native mode on the native application run on the user mobile device with a complete access to a plurality of peripheral wireless devices, and

wherein multiple applications are deployed to the user's mobile device and run as a single application in the user's mobile device,

wherein the application definition file created by the modeler is run as a native application deployed to the mobile device with a consistent experience.

2 . The computerized method of claim 1 , wherein the list of all the nodes that are available comprises a node that defines a start point of the application.

3 . The computerized method of claim 2 , wherein the list of all the nodes that are available comprises a node that defines a slider form for providing a data input in a range of pain rating from 1 to 10.

4 . The computerized method of claim 3 , wherein the list of all the nodes that are available comprises a node that defines an instruction for displaying a set of instructions to the user for inputting data into the application.

5 . The computerized method of claim 4 , wherein the list of all the nodes that are available comprises a node that defines a digital media for displaying an audio and a video content locally from a mobile phone or via the Internet.

6 . The computerized method of claim 5 , wherein the list of all the nodes that are available comprises a node that defines a question to the user for asking a set of Yes/No questions to the user for soliciting additional input from the user.

7 . The computerized method of claim 6 , wherein the list of all the nodes that are available comprises a node that defines an application functionality for displaying a set of digital photos to the user.

8 . The computerized method of claim 7 , wherein the list of all the nodes that are available comprises a node that defines an application functionality for enabling the patient take a digital photograph of a patient injury.

9 . The computerized method of claim 8 , wherein the list of all the nodes that are available comprises a node that defines the application definition file.

10 . The computerized method of claim 9 , wherein the list of all the nodes that are available comprises a node that defines where an execution of the personalized digital software application begins, as user navigates through the personalized digital software application until a stop point of the patient user navigation.

11 . The computerized method of claim 1 , wherein the drag and drop operation is performed by a user.

12 . The computerized method of claim 1 , wherein each node comprises a set of customizable parameters used for tailoring the personalized digital software application.

13 . The computerized method of claim 1 , wherein the deployed application enables a communication session between a care team and the user via an electronic messaging format.

14 . The computerized method of claim 13 , wherein the application displays digital photographs that comprise one or more generic educational photographs or a set of digital photographs specific to a user state.