Ontology-Supported Machine Learning in Biomedicine | Mobile Rehabilitation Application for Health Monitoring

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?