1. Towards Next Generation Integrative Mobile Semantic Health Information Assistants Evan W. Patton (pattoe@rpi.edu), John Sheehan (sheehj4@rpi.edu), Yue Robin Liu (liuy30@rpi.edu), Bassem Makni (maknib@rpi.edu), and Deborah L. McGuinness (dlm@cs.rpi.edu)pattoe@rpi.edusheehj4@rpi.eduliuy30@rpi.edumaknib@rpi.edudlm@cs.rpi.edu Rensselaer Polytechnic Institute / 110 8 th Street / Troy, NY, 12180 USA http://tw.rpi.edu/web/project/MobileHealth Glossary: RPI – Rensselaer Polytechnic Institute TWC – Tetherless World Constellation at Rensselaer Polytechnic Institute EHR / EMR – Electronic Health Record / Electronic Medical Record PHR – Personal Health Record CDC – Center for Disease Control Acknowledgments: MediNet is joint work with Heng Ji. Sponsors: National Science Foundation We aim to build a 24x7 health assistant platform that uses semantic technologies to integrate health data from a variety of resources to support patients in understanding when and how to actively engage in their care, thus potentially increasing a feeling of informed control and empowerment. We motivate the work with a use case helping a newly diagnosed breast cancer patient understand and participate in her treatment plan. We cross- reference data and information in clinical notes with broader medical online resources using natural language processing (e.g., Cross-source Information Extraction, Text Mining and Linking) and Watson-inspired technologies to provide contextualized and referenced information to support browsing and question answering. Use semantic technologies to encode and integrate a wide range of health information to help people function at a level higher than their training. Leverage existing curated and uncurated sources, build reusable integrated content sources and infrastructure By integrating data and providing a semantic, machine- readable and understandable format for health data, we can take advantage of tools such as IBM’s Watson Q&A system by providing its Type Coercion (TyCor) system [1] with relevant class descriptions (either those explicitly declared in existing ontologies or discovered through subsumption inference) of the end user’s medical data and match it to unstructured information from various health- related sources such as the CDC, John Hopkins, the Mayo Clinic, WebMD, and the National Institutes of Health. The result of all the mobile health monitoring efforts are aggregated into a structure we call Medinet. Medinet combines structured health and fitness data with natural language processing techniques on clinical notes, online forums, and medical literature to provide a homogenized representation for all the various types of data and knowledge gathered by out integration efforts. PHR/EHR Physician User Device Applications Mobile Semantic Health Integration Framework Reasoning Services Hardware Abstraction Layer / Device APIs Accelerometer Blood Pressure Heart Rate Sleep PHR/EHR Pedometers Scale Introduction Information are then presented in a variety of web and mobile interfaces to allow patients instant access to their health data. Further, we can use device information, such as location and calendar, to augment the health data in our knowledge base. Use Case A patient observes from her blood report that her Blood Urea Nitrogen has trended above the recommended range. She asks her oncology nurse about this phenomenon and what it might mean for her treatment. The nurse informs her that this is a common occurrence and usually due to dehydration. The patient makes a note in her personal health assistant to track this information further, and the application reminds her that she has a scale at home that can measure water mass in the body as a percentage of overall mass and that her MyFitnessPal application allows her to track her water intake. Using a sensor and an app, the patient collects her own data and becomes more active in monitoring her health. Further, she notices that her blood glucose is higher than normal. Using a BlueTooth™ enabled glucose meter, she takes additional readings over the course of the day that are synchronized to her smartphone and made available as part of an augmented electronic health record. Conclusions Mobile devices and sensors enable new opportunities to empower people to be more proactive about their healthcare and as a means to disseminate personalized information using rich data sources integrated from across the web. Further, advances in question answering systems such as IBM’s Watson provide a vision where smart assistants provide targeted answers based on an individual’s circumstances. Our work makes available many different resources as structured data that can be queried and integrated into new software systems to address the needs of patients. We have prototyped mobile applications to deliver this information along with inferences on whether measurements are within normal ranges and are continuing to explore sensor integration abilities. Data Interfaces Benefits of Semantics Objectives Data Products