Difference between revisions of "Advisory System"
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=Cheetah advisory system= | =Cheetah advisory system= | ||
==Prelude== | ===Prelude=== | ||
Cheetah will generatie advice about insulin dosis or food intake. Several software packages offer this kind of functionality, but none of them an advice as personal as Cheetah will. The system learns from the user and learns how its body reacts on events such as insulin usage, food comsumption and activitivies like sports. The generated KB about the user is then shared (anonymously) with other users over the internet. Likewise, the user receives knowledge from other users. And this is exactly what makes cheetah unique: it will be the first diabetes software that doesnt just learn from one isolated user, but learns from all. This combined with a smart knowledge inferencing system will add up to a complete diabetes health system. | Cheetah will generatie advice about insulin dosis or food intake. Several software packages offer this kind of functionality, but none of them an advice as personal as Cheetah will. The system learns from the user and learns how its body reacts on events such as insulin usage, food comsumption and activitivies like sports. The generated KB about the user is then shared (anonymously) with other users over the internet. Likewise, the user receives knowledge from other users. And this is exactly what makes cheetah unique: it will be the first diabetes software that doesnt just learn from one isolated user, but learns from all. This combined with a smart knowledge inferencing system will add up to a complete diabetes health system. | ||
==Advice== | ===Advice=== | ||
Three types of advice can hypothetically be generated. The first one is short-term, and gives advice about today. The second one tackles long-term problems and generates modifications to the current food/insulin diet. The third kind of advice generates a complete diet for you. | Three types of advice can hypothetically be generated. The first one is short-term, and gives advice about today. The second one tackles long-term problems and generates modifications to the current food/insulin diet. The third kind of advice generates a complete diet for you. | ||
[Type A is a must for Cheetah, while types B and C are optional at the moment] | [Type A is a must for Cheetah, while types B and C are optional at the moment] | ||
===Type A Advice=== | ====Type A Advice==== | ||
Examples: | Examples: | ||
* Ann wants to eat grandma's pie. Give her current glucose level, she wants insulin advice to prevent a hyper. | * Ann wants to eat grandma's pie. Give her current glucose level, she wants insulin advice to prevent a hyper. | ||
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* Cheetah presents the advise. | * Cheetah presents the advise. | ||
===Type B Advice=== | ====Type B Advice==== | ||
Examples: | Examples: | ||
* Ann always has a hypo at night. She wants advice to prevent this to happen again. | * Ann always has a hypo at night. She wants advice to prevent this to happen again. | ||
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* Cheetah presents the cause and gives advice how to solve this. | * Cheetah presents the cause and gives advice how to solve this. | ||
===Type C Advice=== | ====Type C Advice==== | ||
Examples: | Examples: | ||
* Pete has very much hypo's and hypers and wants the systeem to make a compleet food diet for him. | * Pete has very much hypo's and hypers and wants the systeem to make a compleet food diet for him. | ||
| Line 39: | Line 39: | ||
Steps: | Steps: | ||
* [This is a stub] System makes everything a variable and optimizes the curve. | * [This is a stub] System makes everything a variable and optimizes the curve. | ||
===The Glucose Model=== | |||
Lets begin with thinking about Type A Advice and forget type B and C advice. Regardless of the precise implementation, the system needs a way to (trustably) predict if the user will be o.k. given some conditions, and will not be o.k. given some other contitions. For now, lets forget on how this model can be made, and look at the properties of such system. | |||
In such system, the "o.k.-ness" is directly linked with the blood sugar level: these should between some variables, say 5 and 15. Also, the system should be able to say if a person will be o.k. in 1 hour, in 2 hours, in 3 hours etc. More concretely speaking, there should be a ''model'' mapping conditions to glucose levels over time. For each certain interval, say every hour, the model looks at the conditions and predicts blood sugar levels. If, given certain conditions, future glucose levels are optimal then the system knows the conditions are also optimal. | |||
Conditions are things like insulin or food intake, activities, current time, current glucose levels, health status etc. | |||
Conditions come in three kinds. | |||
1. The first ones are certain conditions, from which the effect is certain. Such conditions are current time and current glucose level (if measured). These could also be hard-coded events which effects are certain a priori. | |||
2. The second ones are predicted uncertain conditions. Such conditions have been seen before, and the effect has been estimated (with some certainty). Ann (or someone else in the network) has eaten grandma's applepie before, and the system has estimated the amount of effectivly absorbed carbs. Or: Ann has done a hockey training before, and the system has estimated (with some certainty) the amount of used glucose over the hours. | |||
3. The third ones are conditions which effect are yet unnknown. For example, you're eating something totally exotic and the system cant give an estimation. The effects of such a condition are yet to be estimated. | |||
Revision as of 10:11, 2 May 2006
Cheetah advisory system
Prelude
Cheetah will generatie advice about insulin dosis or food intake. Several software packages offer this kind of functionality, but none of them an advice as personal as Cheetah will. The system learns from the user and learns how its body reacts on events such as insulin usage, food comsumption and activitivies like sports. The generated KB about the user is then shared (anonymously) with other users over the internet. Likewise, the user receives knowledge from other users. And this is exactly what makes cheetah unique: it will be the first diabetes software that doesnt just learn from one isolated user, but learns from all. This combined with a smart knowledge inferencing system will add up to a complete diabetes health system.
Advice
Three types of advice can hypothetically be generated. The first one is short-term, and gives advice about today. The second one tackles long-term problems and generates modifications to the current food/insulin diet. The third kind of advice generates a complete diet for you. [Type A is a must for Cheetah, while types B and C are optional at the moment]
Type A Advice
Examples:
- Ann wants to eat grandma's pie. Give her current glucose level, she wants insulin advice to prevent a hyper.
- Ann will exercise in 2 ours, and wants food advice to prevent a hypo.
Advice Features:
- Advice is generated about insulin dosis or food intake about today (max. 24h ahead).
Steps:
- The user asks for short-term advice
- The system presents a scheme on which the user fills in today's future insulin or food intake. (Needs a intuitive and easy UI which allows the user to choose default values). The user can fill in "?" which are events to be advised.
- Cheetah puts these things into his model and sees how to fill in the ?'s to make the glucose prediction as stable as possible.
- Cheetah presents the advise.
Type B Advice
Examples:
- Ann always has a hypo at night. She wants advice to prevent this to happen again.
- Ann always has a hyper in the evening. She wants advice to prevent his to happen again.
Advice Features:
- Advice is generated not only for today, but is more general. Unlike type A, the (hypo/hyper) problem is recurrent.
Steps:
- The user (and cheetah) spots a hypo/hyper problem and wants a solution.
- Cheetah searches what causes the recurrent problem.
- Cheetah presents the cause and gives advice how to solve this.
Type C Advice
Examples:
- Pete has very much hypo's and hypers and wants the systeem to make a compleet food diet for him.
- Pete goes on a food diet and asks the system to make a new insulin advise for him.
Advice Features:
- Advice consists of a complete new personal diet, generated from scratch.
Steps:
- [This is a stub] System makes everything a variable and optimizes the curve.
The Glucose Model
Lets begin with thinking about Type A Advice and forget type B and C advice. Regardless of the precise implementation, the system needs a way to (trustably) predict if the user will be o.k. given some conditions, and will not be o.k. given some other contitions. For now, lets forget on how this model can be made, and look at the properties of such system. In such system, the "o.k.-ness" is directly linked with the blood sugar level: these should between some variables, say 5 and 15. Also, the system should be able to say if a person will be o.k. in 1 hour, in 2 hours, in 3 hours etc. More concretely speaking, there should be a model mapping conditions to glucose levels over time. For each certain interval, say every hour, the model looks at the conditions and predicts blood sugar levels. If, given certain conditions, future glucose levels are optimal then the system knows the conditions are also optimal. Conditions are things like insulin or food intake, activities, current time, current glucose levels, health status etc. Conditions come in three kinds. 1. The first ones are certain conditions, from which the effect is certain. Such conditions are current time and current glucose level (if measured). These could also be hard-coded events which effects are certain a priori. 2. The second ones are predicted uncertain conditions. Such conditions have been seen before, and the effect has been estimated (with some certainty). Ann (or someone else in the network) has eaten grandma's applepie before, and the system has estimated the amount of effectivly absorbed carbs. Or: Ann has done a hockey training before, and the system has estimated (with some certainty) the amount of used glucose over the hours. 3. The third ones are conditions which effect are yet unnknown. For example, you're eating something totally exotic and the system cant give an estimation. The effects of such a condition are yet to be estimated.