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MobiGuide

The Mobiguide project is a so-called large-scale integrated project (IP) in the Seventh Framework Programme (FP7) of the EU. The project that is being coordinated at the Department of Information Systems at the University of Haifa (HU) by Prof. Mor Peleg from the BMIP group. The project's duration is 4 years and is expected to start on October 1st, 2011.

MobiGuide will develop a patient guidance system that integrates hospital and monitoring data into a personal health record (PHR) accessible by patients and care providers and provide personalized secure clinical-guideline-based guidance also outside clinical environments. MobiGuide's ubiquity will be achieved by having a Decision Support System (DSS) at the back end, and on the front end by utilizing Body Area Network (BAN) technology and developing a coordinated light-weight DSS that can operate independently. Personalization will be achieved by considering patient preferences and context. Retrospective data analysis will be used to assess compliance and to indicate care pathways shown to be beneficial for certain patient context.

MobiGuide will be validated on pre-selected clinical domains with intensive vs. sparse monitoring to demonstrate the generality of the design and assess functionality, feasibility, and impact.

MobiGuide addresses EU priorities: increasing patient safety, ubiquitous secure access to health care, patient empowerment, developing a common platform for healthcare services, and competitiveness of Europe.

The time is right for MG in view of Europe's vast interest in national PHRs and patient empowerment. MobiGuide will leverage this momentum to create a solution that goes beyond local proprietary and stand-alone EMRDSS andBAN.

Our team includes complementary partners with diverse experience in: patient guideline-based DSS, focusing on reasoning with patient guideline intentions and temporal patterns, decision-theoretic models, knowledge-data integration, and information visualization Health BANtelemedicinedata analysis for diabetes, telemedicineapplications for cardiology and expertise in large system integration to create the secure PHR.
Our consortium includes 13 complementary partners from academia, industry, and healthcare institutions in 5 different countries, with diverse experience in: patient guideline-based DSS, focusing on reasoning with patient guideline intentions and temporal patterns, decision-theoretic models, knowledge-data integration, and information visualization Health BANtelemedicinedata analysis for diabetes, telemedicineapplications for cardiology and expertise in large system integration to create the secure PHR. The partners include the University of Haifa Israel, Ben-Gurion University, Israel, University of Pavia, Italy,University of Twente, The Netherlands, Vienna University of Technology, Austia, Mobihealth Co., The Netherlands, Fondazione Salvatore MaugeriClinica Del Lavoro e dellaRiabilitazione, Italy,Polytechnic University of Madrid, Spain, Corporació Sanitaria Parc Tauli, Spain, ATOS Origin, Spain, Beacon Tech Ltd., Israel, ZorgGemakBV, The Netherlands, and Catalonia Diabetic Association, Spain.
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Conte Center for Computational Systems Genomics of Neuropsychiatric Phenotypes

We aim to develop, test, and apply a drastically new computational methodology for the analysis of more than one complex phenotype at a time, with the
goal of generating novel biological results. Specifically, we propose to design and validate a battery of novel analytical tools for the inference of causal relationships among human genomic variations, environmental factors, and more than one mental health phenotype, explicitly exploiting the genetic and
environmental non-independence of complex (multigenic) disorders.

We attempt to consolidate in a single modeling framework a number of disparate approaches for analysis of complex neuropsychiatric disorders. The comprehensive modeling approach will produce experimentally testable predictions, a considerable number of which we will be able to validate within the proposed research. We will focus on several phenotypes with major impacts on the health of US populations, such as anxiety, schizophrenia and depression.

This research is part of the NIH-funded Conte Center for Computational Systems Genomics of Neuropsychiatric Phenotypes which is a collaboration between researchers from the University of Chicago (Andrey Rzhetsky, Nancy Cox), Harvard University (Zak Kohane), Stanford University (Russ Altman), Columbia University (Raul Rabadan), University of Illinois (Edwin Cook), Northwestern University (Richard Morimoto), and the University of Haifa, Israel (Mor Peleg).

Learning Process Model (LPM)

Learning Process Model (LPM) is a process learning technique based on artificial intelligence methods that has been applied to suggest context-based improvements to a clinical care process model based on learning from past process executions and their outcomes.

Publications:

Mor Peleg, Pnina Soffer, Johny Ghattas. "Mining Process Execution and Outcomes". Business Process Modeling Conference Workshop: 1st International Workshop on Process-oriented Information Systems in Healthcare, Brisbane, Australia. September 2007.[abstractfull paper pdf]

Johny Ghattas, Pnina Soffer, Mor Peleg. "Learning Business Process Models: A case study". Business Process Modeling Conference Workshop: 1st International Workshop on Process-oriented Information Systems in Healthcare, Brisbane, Australia. September 2007.[abstractfull paper pdf]

Johny Ghattas, Pnina Soffer, Mor Peleg." A Goal-based approach for business process learning". Accepted for publication in the Workshop on Business Process Modeling, Development, and Support (BPMDS'08), in conjunction with CAISE'08, 16-17 June 2008, Montpellier, France [full paper pdf]

Pnina Soffer, Johny Ghattas, and Mor Peleg . A Goal-based Approach for Learning in Business Processes. In: Intentional Perpectives on Information
Systems Engineering, Camille Salinesi, Carine Souveyet, Jolita Ralyte editors. Springer. February 2010 [full paper pdf].

MobiGuide general presentation

unified form of the projects' presentation

MG-Presentation1.pptx — application/vnd.openxmlformats-officedocument.presentationml.presentation, 1010 kB (1034943 bytes)