Systems and methods for disease control and management

• US 10,872,686 B2
• Filed: 04/21/2020
• Issued: 12/22/2020
• Est. Priority Date: 02/22/2007
• Status: Active Grant

• Current Assignee: WellDoc, Inc
• Original Assignee: WellDoc, Inc
• Inventors: Sysko, Ryan A., Sysko, Suzanne K., Minor, James M., Iyer, Anand K., Fletcher, Andrew V.
• Primary Examiner(s): Reyes, Reginald R
• Application Number:
  US16/854,126
• Publication Number:
  US 20200251199A1
• Time in Patent Office: 245 Days
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A system for creating and updating one or more models for providing treatment recommendations for health conditions may receive weight measurements from a weight measuring device, execute a basic model based on information regarding the health condition, to generate treatment recommendations, generate reminders prompting the user to obtain weight measurements, estimate an impact of the user data on the weight measurements, generate a modified model, based on the basic model, the received weight measurements, the received user data, and the estimated impact of the user data, the modified model including personalized treatment recommendations specific to the user, and transmit the reminders, the treatment recommendations, and the personalized treatment recommendations to the user device.

Claims

1. A system for creating and updating one or more models for providing treatment recommendations for health conditions, comprising:

a weight measuring device configured to obtain weight measurements and to wirelessly transmit the obtained weight measurements; and

a disease management server in wireless communication with the weight measuring device, the disease management server including;

a receiver for receiving the weight measurements transmitted from the weight measuring device, and configured to receive user data from a user device; and

one or more processors configured to;

execute a basic model of a physiological system generic to any user to generate a modified model specific to a physiological system of the user, wherein generating the modified model comprises updating the basic model based on information specific to the user;

perform a statistical analysis to detect a data excursion in the received weight measurements, wherein a data excursion is detected when a weight measurement, of the received weight measurements, collected at a particular time, is outside of a predetermined range;

execute the modified model to generate personalized notifications, based on the modified model, wherein executing the modified model comprises determining one or more factors causing a detected data excursion, and developing the personalized notifications based on the determined one or more factors that caused a detected data excursion, the personalized notifications including adjusting a timing of obtaining weight measurements;

generate an updated modified model, at least once, based on the modified model, wherein generating the updated modified model comprises training the modified model with the determined one or more factors learned from the received weight measurements to update an ability of the modified model, in the updated modified model, to estimate an impact of changes in the one or more factors on the physiological system of the user;

perform the statistical analysis to detect a data excursion in received additional weight measurements, wherein a data excursion in the received additional weight measurements is detected when a weight measurement, of the received additional weight measurements, collected at a particular time, is outside of the predetermined range;

execute the updated modified model to generate updated personalized notifications, wherein executing the updated modified model comprises determining one or more factors causing a detected data excursion in the received additional weight measurements, and developing the updated personalized notifications based on the determined one or more factors that caused a detected data excursion in the received additional weight measurements, the updated personalized notifications including adjusting a timing of obtaining weight measurements; and

output the personalized notifications and the updated personalized notifications to at least one of the weight measuring device and the user device,

wherein each of the weight measuring device and the user device is configured to receive and to output the personalized notifications and the updated personalized notifications to the user.

2. The system according to claim 1, wherein the receiver receives the weight measurements every time the weight measuring device transmits a weight measurement to the disease management server.

3. The system according to claim 1, wherein the receiver receives the weight measurements at predetermined intervals.

4. The system according to claim 1, wherein the user data includes a food and drink caloric intake and exercise data of the user.

5. The system according to claim 1, wherein the personalized notifications and the updated personalized notifications further include one or more of advice to modify behavior and educational materials.

6. A computer-implemented method for creating and updating one or more models for providing personalized notifications for health conditions, the method comprising:

receiving, using a receiver of a disease management server, weight measurements from a weight measuring device wirelessly connected to the disease management server, and user data of the user, from a user device;

executing, using one or more processors of the disease management server, a basic model of a physiological system generic to any user to generate a modified model specific to a physiological system of the user, wherein generating the modified model comprises updating the basic model based on information specific to the user;

performing, using the one or more processors, a statistical analysis to detect a data excursion in the received weight measurements, wherein a data excursion is detected when a weight measurement, of the received weight measurements, collected at a particular time, is outside of a predetermined range;

executing, using the one or more processors, the modified model to generate personalized notifications, based on the modified model, wherein executing the modified model comprises determining one or more factors causing a detected data excursion, and developing the personalized notifications based on the determined one or more factors that caused a detected data excursion, the personalized notifications including adjusting a timing of obtaining weight measurements;

generating, using the one or more processors, an updated modified model, at least once, based on the modified model, wherein generating the updated modified model comprises training the modified model with the determined one or more factors learned from the received weight measurements to update an ability of the modified model, in the updated modified model, to estimate an impact of changes in the one or more factors on the physiological system of the user;

performing, using the one or more processors, the statistical analysis to detect a data excursion in received additional weight measurements, wherein a data excursion in the received additional weight measurements is detected when a weight measurement, of the received additional weight measurements, collected at a particular time, is outside of the predetermined range;

executing the updated modified model to generate updated personalized notifications, wherein executing the updated modified model comprises determining one or more factors causing a detected data excursion in the received additional weight measurements, and developing the updated personalized notifications based on the determined one or more factors that caused a detected data excursion in the received additional weight measurements, the updated personalized notifications including adjusting a timing of obtaining weight measurements; and

outputting the personalized notifications and the updated personalized notifications to at least one of the weight measuring device and the user device; and

outputting, using the weight measuring device or the user device, the personalized notifications and the updated personalized notifications to the user.

7. The method according to claim 6, wherein the weight measurements are received every time the weight measuring device transmits a weight measurement to the disease management server.

8. The method according to claim 6, wherein the weight measurements are received at predetermined intervals.

9. The method according to claim 6 wherein the user data includes a food and drink caloric intake and exercise data of the user.

10. A system for creating and updating one or more modified models for providing personalized notifications for health conditions, comprising:

a glucose measuring device configured to obtain glucose measurements from a user, and to wirelessly transmit the obtained glucose measurements;

a weight measuring device configured to obtain weight measurements from the user, and to wirelessly transmit the obtained weight measurements; and

a disease management server in wireless communication with the glucose measuring device and the weight measuring device, the disease management server comprising;

a receiver for receiving the glucose measurements, the weight measurements, and user data, from a user device; and

one or more processors configured to;

execute a basic model of a physiological system generic to any user to generate one or more modified models specific to a physiological system of the user, wherein generating the one or more modified models comprises updating the basic model based on information specific to the user;

perform a statistical analysis to detect a data excursion in at least one of the received glucose measurements and the received weight measurements, wherein a data excursion is detected when a glucose measurement, of the received glucose measurements, collected at a particular time, is outside of a predetermined range of glucose measurements, or a weight measurement, of the received weight measurements, is outside of a predetermined range of weight measurements;

execute the one or more modified models to generate personalized notifications, based on the one or more modified models, wherein executing the one or more modified models comprises determining one or more factors causing a detected data excursion, and developing the personalized notifications based on the determined one or more factors that caused a detected data excursion, the personalized treatment recommendations including one or more of adjusting a timing of obtaining the glucose measurements and adjusting a timing of obtaining the weight measurements;

generate one or more updated modified models, based on the one or more modified models, wherein generating the one or more updated modified models comprises training the one or more modified models with the determined one or more factors learned from the received glucose measurements and the received weight measurements, to update an ability of the one or more modified models, in the corresponding one or more updated modified models, to estimate an impact of changes in the one or more factors on the physiological system of the user;

perform the statistical analysis to detect a data excursion in at least one of received additional glucose measurements and additional weight measurements, wherein a data excursion is detected when a glucose measurement, of the received additional glucose measurements, collected at a particular time, is outside of the predetermined range of glucose measurements, or a weight measurement, of the received additional weight measurements, is outside of the predetermined range of weight measurements;

execute the one or more updated modified models, to generate updated personalized notifications, wherein executing the one or more updated modified models comprises determining one or more factors causing a detected data excursion in the at least one of the received additional glucose measurements and the additional weight measurements, and developing the updated personalized notifications based on the determined one or more factors that caused a detected data excursion in the received additional glucose measurements or the additional weight measurements, the updated personalized notifications including at least one of adjusting a timing of obtaining the glucose measurements and adjusting a timing of obtaining the weight measurements; and

output the personalized notifications and the updated personalized notifications to at least one of the glucose measuring device, the weight measuring device, and the user device,

wherein each of the glucose measuring device, the weight measuring device, and the user device is further configured to receive and to output the personalized notifications and the updated personalized notifications to the user.

11. The system according to claim 10, wherein the receiver receives a glucose measurement from the glucose measurement device every time a glucose measurement is obtained and a weight measurement from the weight measuring device every time a weight measurement is obtained.

12. The system according to claim 10, wherein the receiver receives each of the obtained glucose measurements and the obtained weight measurements at predetermined intervals.

13. The system according to claim 10, wherein the user data includes a food and drink caloric intake and exercise data of the user.

14. The system according to claim 13, wherein at least one of the personalized notifications and the updated personalized notifications further includes exercise recommendations for the user.

15. The system according to claim 13, wherein at least one of the personalized notifications and the updated personalized notifications further includes one or more of advice to modify behavior or educational materials.

16. The system according to claim 10, wherein the one or more processors are further configured to output an alert to a provider associated with the user.

17. The system according to claim 16, wherein the one or more processors output an alert to the provider associated with the user when a measurement value of at least one of the received glucose measurements and the received weight measurements is outside a range of normal measurement values.

18. The system according to claim 10, wherein the glucose measuring device transmits the glucose measurements, the weight measuring device transmits the weight measurements, and the disease management server transmits the personalized notifications and the updated personalized notifications via one of a cellular channel or a wireless network.

19. The system according to claim 10, wherein the glucose measuring device is a continuous glucose monitor.

20. The system according to claim 10, wherein at least one of the personalized notifications and the updated personalized notifications includes adjusting a medication intake.