MONITORING AND TREATMENT DOSAGE PREDICTION SYSTEM

• US 20170091419A1
• Filed: 06/02/2016
• Published: 03/30/2017
• Est. Priority Date: 09/25/2015
• Status: Active Grant

• Current Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Original Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Inventors: HOGLUND, Jonas, Mbanefo, Primrose, O'Kane, Matthew, Johansson, Henrik
• Granted Patent: US 10,657,224 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 20/13 delivered from dispensers
• CPC Class Codes: G16H 20/17 delivered via infusion or injection
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/60 relating to nutrition control, e.g. diets
• CPC Class Codes: G16H 20/70 relating to mental therapies, e.g. psychological therapy or autogenous training
• 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

Abstract

A device may receive lifestyle information corresponding to an individual with a medical condition. The device may create a lifestyle timeline, associated with the individual, that includes a plurality of items of lifestyle information, included in the lifestyle information, arranged in a chronological order. The device may generate, based on the lifestyle timeline, a recommendation or a prediction associated with a metric related to the medical condition. The recommendation may be generated based on one or more of the plurality of items of lifestyle information. The prediction may be generated using a prediction model that uses one or more of the plurality of items of lifestyle information as input, and generates the prediction using the prediction model. The device may provide information associated with the recommendation or the prediction to identify a dosage associated with treating or managing the medical condition

Claims

1. A method, comprising:

receiving, by a device and from at least one of a user device or a server device, lifestyle information corresponding to an individual with a medical condition;

creating, by the device and based on the lifestyle information, a lifestyle timeline associated with the individual,

the lifestyle timeline;

including a plurality of items of lifestyle information, included in the lifestyle information, and

having the plurality of items of lifestyle information arranged in a chronological order;

generating, by the device and based on the lifestyle timeline, a recommendation or a prediction associated with a metric related to the medical condition,

the recommendation being generated based on one or more of the plurality of items of lifestyle information, or

the prediction being generated using a prediction model that uses one or more of the plurality of items of lifestyle information as input; and

providing, by the device and to the user device, information associated with the recommendation or the prediction,

the information associated with the recommendation or the prediction being provided to;

cause a notification to be provided by the user device at, at least one of;

a first particular time prior to the metric being predicted to reach a threshold, or

a second particular time when the metric is predicted to reach the threshold, and

identify a dosage associated with treating or managing the medical condition,

the dosage being identified based on the recommendation or the prediction.

2. The method of claim 1, further comprising:

determining a set of predictive factors associated with the medical condition,

a predictive factor, of the set of predictive factors, being a factor or an amount by which the metric is predicted to be affected by an item of lifestyle information of the plurality of items of lifestyle information; and

where generating the recommendation or the prediction associated with the metric comprises;

generating the recommendation or the prediction based on the set of predictive factors.

3. The method of claim 1, where the plurality of items of lifestyle information includes a combination of historical items of lifestyle information, current items of lifestyle information, and future items of lifestyle information.

4. The method of claim 1, where the recommendation includes a recommended dosage of a treatment associated with the medical condition.

5. The method of claim 1, where the prediction includes a predicted value of the metric associated with the medical condition at a future time.

6. The method of claim 1, where receiving the lifestyle information comprises:

receiving the lifestyle information based on user input via the user device associated with the individual.

7. The method of claim 1, where receiving the lifestyle information comprises:

automatically receiving the lifestyle information from the user device associated with the individual.

8. A device, comprising:

one or more processors to;

receive, from at least one of a user device or a server device, lifestyle information corresponding to an individual with a medical problem;

create, based on the lifestyle information, a lifestyle timeline associated with the individual,

the lifestyle timeline;

including a plurality of items of lifestyle information included in the lifestyle information, and

having the plurality of items of lifestyle information arranged in a chronological order;

generate, based on the lifestyle timeline, a recommendation or a prediction associated with a health metric related to the medical problem,

the recommendation including a recommended dosage of a treatment associated with the health metric and being generated based on one or more of the plurality of items of lifestyle information, or

the prediction including a predicted value of the health metric at a future time and being generated by a prediction model that uses one or more items of the plurality of items of lifestyle information as input; and

provide, to the user device, information associated with the recommendation or the prediction,

the information associated with the recommendation being provided to;

cause a notification to be provided by the user device, at least one of;

a first particular time prior to the health metric being predicted to reach a threshold, or

a second particular time when the health metric is predicted to reach the threshold, and

identify the recommended dosage or the predicted value of the health metric.

9. The device of claim 8, where the one or more processors are further to:

determine, based on the lifestyle timeline, a current state associated with the individual; and

identify, using a nearest neighbor analysis, a historical state similar to the current state; and

where the one or more processors, when generating the recommendation or the prediction, are to;

generate the recommendation or the prediction based on a historical recommendation or a historical prediction corresponding to the historical state.

10. The device of claim 8, where the one or more processors, when receiving the lifestyle information, are to:

receive the lifestyle information from the server device,

the server device having access to the lifestyle information.

11. The device of claim 8, where the one or more processors are further to:

merge, combine, or normalize the lifestyle information to create converted lifestyle information,

the converted lifestyle information being formatted to permit creation of the lifestyle timeline; and

where the one or more processors, when creating the lifestyle timeline, are to;

create the lifestyle timeline based on the converted lifestyle information.

12. The device of claim 8, where the one or more processors are further to:

determine, based on the lifestyle information, a current value associated with the health metric; and

where the one or more processors, when generating the recommendation or the prediction, are to;

generate the recommendation or the prediction based on the current value of the health metric.

13. The device of claim 8, where the one or more processors, when receiving the lifestyle information, are to:

receive the lifestyle information from the user device,

the user device being a device that is worn by the individual.

14. The device of claim 8, where the lifestyle information includes at least one of:

event information from a schedule or calendar associated with the individual;

sleep information associated with the individual;

information associated with an activity in which the individual has participated, is participating in, or is to participate;

information associated with a diet of the individual;

biological information associated with the individual;

information associated with an emotional state of the individual;

medical information, associated with treating the medical problem, corresponding to the individual;

information associated with a location of the individual;

environmental information associated with the individual;

or

information associated with one or more transactions associated with the individual.

15. A non-transitory computer-readable medium storing instructions, the instructions comprising:

one or more instructions that, when executed by one or more processors, cause the one or more processors to;

receive, from at least one of a user device or a server device, lifestyle information corresponding to an individual with a medical problem;

create, based on the lifestyle information, a lifestyle timeline associated with the individual,

the lifestyle timeline;

including a plurality of items of lifestyle information included in the lifestyle information, and

having the plurality of items of lifestyle information arranged in a chronological order;

determine, based on the lifestyle information, a current value of a metric associated with the medical problem;

generate, based on the lifestyle timeline and the current value of the metric, a recommendation or a prediction associated with the metric,

the recommendation being generated based on a first set of the plurality of items of lifestyle information, or

the prediction being generated using a predictive model that uses a second set of the plurality of items of lifestyle information as input; and

provide, to the user device, information associated with the recommendation or the prediction,

the information associated with the recommendation or the prediction being provided to;

cause a notification to be provided by the user device at, at least one of;

a first particular time prior to the metric being predicted to reach a threshold, or

a second particular time when the metric is predicted to reach the threshold, and

identify a dosage, associated with the medical problem, or the prediction associated with the metric.

16. The non-transitory computer-readable medium of claim 15, where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

receive updated lifestyle information corresponding to the individual,

the updated lifestyle information being different from the lifestyle information;

update the lifestyle timeline, based on the updated lifestyle information, to create an updated lifestyle timeline;

generate, based on the updated lifestyle timeline, an updated recommendation or an updated prediction associated with the metric; and

provide information associated with the updated recommendation or the updated prediction.

17. The non-transitory computer-readable medium of claim 15, where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

determine a set of predictive factors associated with the medical problem,

a predictive factor, of the set of predictive factors, being a factor or an amount by which the metric is predicted to be affected by an item of lifestyle information of the plurality of items of lifestyle information; and

where the one or more instructions, that cause the one or more processors to generate the recommendation or the prediction, cause the one or more processors to;

generate the recommendation or the prediction further based on the set of predictive factors.

18. The non-transitory computer-readable medium of claim 15, where the recommendation identifies a recommended time at which a recommended dosage of a treatment, associated with the medical problem, should be administered.

19. The non-transitory computer-readable medium of claim 15, where the prediction includes a predicted value of the metric at a future time.

20. The non-transitory computer-readable medium of claim 15, where the plurality of items of lifestyle information includes at least two of:

historical items of lifestyle information;

current items of lifestyle information;

or

future items of lifestyle information.