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Rasheed Rabbi

Ontology – Supported Machine Learning and Decision Support in Biomedicine Alexey Tsymbal Sonja Zillner Martin Huber. Rasheed Rabbi. Presentation Outline. Research Goal Used example or case study Key Idea and Key words Health Care knowledge repository Questions. Research Goal.

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Rasheed Rabbi

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  1. Ontology – Supported Machine Learning and Decision Support in BiomedicineAlexeyTsymbalSonja ZillnerMartin Huber Rasheed Rabbi

  2. Presentation Outline • Research Goal • Used example or case study • Key Idea and Key words • Health Care knowledge repository • Questions

  3. Research Goal How ontology and Machine Learning can help extracting useful knowledge

  4. Case Study: • Context: Health-e-child participants in 2006 • Source: biomedical information from genetic  clinical  epidemiological • Goal: improve children disease prevention, screening, early diagnosis therapy and follow up of pediatric diseases • Methodology: Large Data Input   Complex pattern recog Machine Learning

  5. Case Study: • Diseases of 3 categories: • pediatric heart disease • inflammatory disease • brain tumors • Pediatric Heart DiseaseAtrial Septal Defect (ASD) • Hole in Atrial septum • Treatment needs to happen from 4-6 years of age • The prognosis of ASD depends on heterogeneous feature of clinical data, genetic data, ECG and imaging data

  6. Case Study

  7. Key Concepts: • Ontology: Philosophy to describe the nature, categories and the relationship objects • Feature Ontology: • Reflects both the semantic and linguistic neighborhoods of a particular entity. • Constitutes a rich representation of an entity • Hierarchical structure in tree graph where N is the node, l is the level and w is the weight. • Ontology  Ontology feature

  8. Health Care knowledge repository

  9. Questions • Any Questions?

  10. A Mobile Rehabilitation Application for the Remote Monitoring of Cardiac Patients after a Heart Attack or a Coronary Bypass SurgeryValérie GayPeter LeijdekkersEdward Barin Rasheed Rabbi

  11. Outline • Introduction to mobile rehabilitation application for remote monitoring • PHM (Personal Health Monitor) • Scenario • Interface • Remote Sharing

  12. Personal Health Monitor • Ambulatory monitoring • Multiple Sensors • Personalization • Instant Feedback • Software running locally on the phone • Arrhythmia Detection • Reminders and logs • Communication • Remote Monitoring via Health Care Data server

  13. Scenario • Jack walks 6 minutes to determine: • RPE • O2 saturation • Can be monitored more than twice a week through his cell phone which has pairs of Bluetooth sensors. • He wears the heart monitor and use the mobile monitoring application during exercise • It allows him to be familiar with the application while being supervised • After 2 weeks, he has gained enough confidence to do the exercise in local gym using mobile rehabilitation app • He synchronize data every week with health center

  14. User Interface • Three parts: • Live data • Configuration • Rehabilitation

  15. Remote Monitoring • User can monitor on their own • Their data can be uploaded to a remote site • Synchronization between the mobile phone and website happens using 3G. • Website is secure and accessible to patient and health professionals • Alert generate measurements are over threshold

  16. Questions • Any Questions?

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