Mobile Medicine

1. Mobile Medicine Pierfrancesco Belline Ivan Bruno Daniele Cenni Alice Fuzier Paolo Nesi Michela Paolucci Semantic computing management for health care applications on desktop and mobile devices

2. Continuous Improvement in Healthcare • The ubiquity of mobile devices has presented new opportunities in distributing multimedia content • The medical community is continuously upgrading procedures and techniques. • Mobile Medicine integrates semantic computing with content distribution technologies in order to provide relevant content to HealthCare Providers. • during emergencies • for continuing education

3. System Architecture Monitoring & Reporting Content Production and Usage` AXMEDIS DRM Servers Grid Scheduler MobileMedicine Frontend IPR Management Node 1 User Management Node 2 Mobile Support Nodes ... Indexing/Querying Node N Recommendations Mobile Medicine CNP Databases Back Office Front-end Portal and Services Users

4. Suggestions (computed) • Computationally expensive because of – • Large N • Elements of N are complex • O(N^2) or worse if elements of N are complex • Estimation of similarity between elements using the following types of information • Users – static & dynamic information • Content – static (more so) & dynamic information • Descriptors will change over time based on users’ actions and will need to update the distances (similarities)

5. Additional Complexities... • Crossmedia descriptors can be very complex • Adding new elements requires the computation of many new similarity distances O(GM) Cross media content package. PDF HTML Flash

6. Clustering techniques • Clustering reduces the complexity of similarity distance calculation • K means (works only with numeric values) • assume K clusters • assume K centers (centroids) • assign each point to closest centroid • sum distances of each point • minimize that value by calculating new centroids* • reassign points to centroids • iteratively to convergence (clusters have minimal or no changes) • O(NKI) complexity • Select elements randomly from that cluster –or- • Do the calculation on a smaller set * vector calculus beyond scope of discussion and this presenter

7. Adding a new element... • Calculate a new element’s similarity to each cluster center. • Determine the cluster with minimum difference and place the element in that cluster. • Periodically recalculate clusters to optimize system with new elements.

8. What if your data is not numeric? • Distance can be the number of matched keywords • Distance based on keyword relationship in an ontology or taxonomy antimicrobial antiviral antibiotic An ontology is mapped onto a tree and distances are calculated based on the tree structure. oral oral intravenous intravenous

9. MobileMedicine Solution • MobileMedicine uses a new method of clustering which is hierarchical evolution of the k-medoids • k-medoids: similar to k-means, but each center point (medoid) is switched out until the lowest averaged distance is found, repeat until no medoid changes • hierarchical: an agglomerative or “bottom up” approach starts with each point as its own cluster, then begins to join points into clusters based on an increasing distance

10. Calculating Similarity DistancesD(U1:U2)=ksSxiSdi(U1:U2)+kdSyiDdi(U1:U2) T`s T`D • Sdi are static distance metrics: languages, age, continent/nationality, Groups, specializations • Ddi are dynamic distance metrics: set as friend, recommend, unfriend • ks and kd are weighting factors • Ts and Td are number of features to estimate similarity distance

11. Experiments • Different algorithms were used to cluster Content objects: • k-medoids, • hierarchical with complete linkage (farthest neighbor) • hierarchical with single linkage (nearest neighbor) • hierarchical with average linkage. • These clusters were then used to provide suggestions: C->U, C->G, and C->C

12. Conclusions • A solution has been presented to take advantage of semantic computing capabilities in order to give health care providers meaningful content on their mobile devices. • Questions???