PRIME - Revision history

Slawek: Reverted edits by Slawek (talk) to last revision by Pabmor

2017-11-23T22:19:11Z

Reverted edits by Slawek (talk) to last revision by Pabmor

Slawek at 22:17, 23 November 2017

2017-11-23T22:17:59Z

Pabmor at 16:28, 16 November 2017

2017-11-16T16:28:41Z

Pabmor at 16:26, 16 November 2017

2017-11-16T16:26:37Z

Pabmor at 16:26, 16 November 2017

2017-11-16T16:26:03Z

Pabmor at 16:25, 16 November 2017

2017-11-16T16:25:00Z

Pabmor at 16:20, 16 November 2017

2017-11-16T16:20:08Z

Pabmor at 16:19, 16 November 2017

2017-11-16T16:19:53Z

Pabmor at 16:19, 16 November 2017

2017-11-16T16:19:38Z

Pabmor at 16:19, 16 November 2017

2017-11-16T16:19:16Z

@@ Line 1: / Line 1: @@
 {{ResearchProjInfo
-|Title=iMedA
+|Title=PRIME
-|ContactInformation=Slawomir Nowaczyk
+|ContactInformation=Pablo del Moral
-|ShortDescription=Improving MEDication Adherence through Person Centered Care and Adaptive Interventions
+|ShortDescription=PRedictive Intelligent Maintenance Enabler
-|Description=The iMedA project will improve medication adherence for hypertensive patients through an AI agent that supports doctor and patient in collaboratively understanding key individual adherence risk factors and designing an appropriate intervention plan. iMedA will deliver the selected intervention through a mobile App and follow-up on its effectiveness improving the system over time. The combination of person-centered care and self-management interventions will lead to significantly improved health outcomes and reduced healthcare costs.
+|Description=The goal of this project is predicting failures in a fleet of sterilizers deployed in hospitals all over the world. The characteristics of this problem are general to the field of predictive maintenance for different application fields.
-iMedA empowers hypertensive patients to take responsibility for their health through self-management, and provides doctors with information they need for person-centered care. To identify risks and intervention strategies, iMedA uses health records as well as self-reported input. The AI agent understands how both medical and personal factors interact with respect to medication adherence, and display this information on a "dashboard" that guides patient-doctor conversation. The AI monitors the effectiveness of interventions in order to improve over time.
+Companies are interested in predictive maintenance to reduce the down time of their machines. In general the list of critical components, whose unexpected breakdowns would result in stopping the machine, is long. Therefore, the scope of a predictive maintenance system should be predicting failures in a big number of different components.
-The iMedA agent will be built by combining three important AI techniques. First is to create a meaningful and comprehensive representation of each patient based on information fusion and representation learning. Second is to use peer group analysis and interpretable supervised machine learning methods to predict non-adherence for concrete patients. Finally, intervention strategies that are the most appropriate for a particular patient we will selected by combining data-driven and knowledge-driven approaches.
+For several years, systems such as cars, sterilizers or industrial equipment have been equipped with a significant amount of sensors. Which signals to record is in general not decided based on the predictive maintenance needs, but on the requirements of security or controllers among other reasons. The sensors mounted usually don’t describe the particular behavior of the components of interest, but measure physical quantities that can be influenced by the different behavior of several components.
 |LogotypeFile=Sterilizer photo.png
-|ProjectResponsible=Slawomir Nowaczyk
+|ProjectResponsible=Pablo
-|ProjectStart=2017/11/20
+|ProjectStart=2016/09/01
-|ProjectEnd=2020/11/19
+|ProjectEnd=2021/03/01
-|ApplicationArea=
+|ApplicationArea=Intelligent Vehicles
 }}
 __NOTOC__
 {{ShowResearchProject}}
-[[Image:iMedA.png|250px]]

@@ Line 1: / Line 1: @@
 {{ResearchProjInfo
-|Title=PRIME
+|Title=iMedA
-|ContactInformation=Pablo del Moral
+|ContactInformation=Slawomir Nowaczyk
-|ShortDescription=PRedictive Intelligent Maintenance Enabler
+|ShortDescription=Improving MEDication Adherence through Person Centered Care and Adaptive Interventions
-|Description=The goal of this project is predicting failures in a fleet of sterilizers deployed in hospitals all over the world. The characteristics of this problem are general to the field of predictive maintenance for different application fields.
+|Description=The iMedA project will improve medication adherence for hypertensive patients through an AI agent that supports doctor and patient in collaboratively understanding key individual adherence risk factors and designing an appropriate intervention plan. iMedA will deliver the selected intervention through a mobile App and follow-up on its effectiveness improving the system over time. The combination of person-centered care and self-management interventions will lead to significantly improved health outcomes and reduced healthcare costs.
-Companies are interested in predictive maintenance to reduce the down time of their machines. In general the list of critical components, whose unexpected breakdowns would result in stopping the machine, is long. Therefore, the scope of a predictive maintenance system should be predicting failures in a big number of different components.
+iMedA empowers hypertensive patients to take responsibility for their health through self-management, and provides doctors with information they need for person-centered care. To identify risks and intervention strategies, iMedA uses health records as well as self-reported input. The AI agent understands how both medical and personal factors interact with respect to medication adherence, and display this information on a "dashboard" that guides patient-doctor conversation. The AI monitors the effectiveness of interventions in order to improve over time.
-For several years, systems such as cars, sterilizers or industrial equipment have been equipped with a significant amount of sensors. Which signals to record is in general not decided based on the predictive maintenance needs, but on the requirements of security or controllers among other reasons. The sensors mounted usually don’t describe the particular behavior of the components of interest, but measure physical quantities that can be influenced by the different behavior of several components.
+The iMedA agent will be built by combining three important AI techniques. First is to create a meaningful and comprehensive representation of each patient based on information fusion and representation learning. Second is to use peer group analysis and interpretable supervised machine learning methods to predict non-adherence for concrete patients. Finally, intervention strategies that are the most appropriate for a particular patient we will selected by combining data-driven and knowledge-driven approaches.
 |LogotypeFile=Sterilizer photo.png
-|ProjectResponsible=Pablo
+|ProjectResponsible=Slawomir Nowaczyk
-|ProjectStart=2016/09/01
+|ProjectStart=2017/11/20
-|ProjectEnd=2021/03/01
+|ProjectEnd=2020/11/19
-|ApplicationArea=Intelligent Vehicles
+|ApplicationArea=
 }}
 __NOTOC__
 {{ShowResearchProject}}
+[[Image:iMedA.png|250px]]

@@ Line 17: / Line 17: @@
 |ProjectEnd=2021/03/01
 |ApplicationArea=Intelligent Vehicles
 }}
 __NOTOC__

@@ Line 3: / Line 3: @@
 |ContactInformation=Pablo del Moral
 |ShortDescription=PRedictive Intelligent Maintenance Enabler
 |LogotypeFile=Sterilizer photo.png
 |ProjectResponsible=Pablo
@@ Line 15: / Line 24: @@
 [[Image:Sterilizer photo.png|150px]]

@@ Line 3: / Line 3: @@
 |ContactInformation=Pablo del Moral
 |ShortDescription=PRedictive Intelligent Maintenance Enabler
 |LogotypeFile=Sterilizer photo.png
 |ProjectResponsible=Pablo
@@ Line 24: / Line 15: @@
 [[Image:Sterilizer photo.png|150px]]

← Older revision		Revision as of 16:20, 16 November 2017
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	[[Image:Sterilizer photo.png\|150px]]		[[Image:Sterilizer photo.png\|150px]]
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−	~~%%[[Image:Sterilizer photo.png\|300px]]~~
−	~~</p>~~

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 <p align="center">
-##[[Image:Sterilizer photo.png|300px]]
+%%[[Image:Sterilizer photo.png|300px]]
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