1. Personalized Medicine Research at the University of Rochester Henry Kautz Department of Computer Science

2. Personalized Medicine Smart Sensing Intelligent Information Management Effective Interfaces Putting the patient at the center of their health system Family & Friends Healthcare Providers Web Repositories Healthcare Institutions Researchers

3. Smart Sensing Non-invasive wearable sensors Personal biosensors Environmental sensors New data streams + machine learning = “New vital signs” Invasive -Implant -Biopsy Non-Invasive - BP, HR, … -Imaging -Smart materials Ambient -Motion - Activity -Sound - Interaction

4. Intelligent Information Management Longitudinal data – Personal baseline – Detect trends & deviations from norm Personal health records – Privacy – Sharing – Anonymous aggregation Patient-centered decision support

5. Effective Interfaces Multimodal – GUI, touch, gesture, speech, … Mobile – Portable, networked, wearable Intuitive – Easy to learn, use, maintain Adaptive Proactive

6. Computational Challenges Understanding human behavior from sensor data – Integrating vastly different kinds of data E.g.: RFID touch sensor, machine vision, EKG – Incorporating commonsense knowledge – Compute intensive methods for learning & inference Embedded, mobile, and distributed systems – Data transport in dynamic, heterogeneous environments E.g.: Data collected indoors, outdoors, laboratories, homes – Security and data sharing Patient, doctor, family, researchers, … – Data / annotation / interpretation streams

7. University of Rochester Center for Future Health – Interdisciplinary center for proactive healthcare technology – Researchers from Strong Medical Center, UR Electrical & Computer Engineering, UR Computer Science Laboratory for Assisted Cognition Environments (LACE) – New (2007) effort focuses on applying AI and machine learning to technology to help cope with cognitive disabilities

8. 8 System Concept Personal Health Monitoring PHASE Project Create a prototype proactive personal health monitoring system for cardiac patients –Determine the value of prognostics in chronic care management Borrow from the field of machine health monitoring –Identify the most minimally invasive ways to capture data –Mine collected data to identify personal baselines, data defined models and track changes –Identify patient preferences and create a system that gives a valuable user experience –Identify effective ways to share data with health care providers Measure Cardiac function (ECG) Respiration (Sound/ECG) Activity (Accelerometry) Alivetec ECG & motionTouch screen mobile phone

9. Personal Health Management Assistant Provide effective, intuitive access to information in Personal Health Record True conversational interaction – UR Computer Science leading center of research on dialog systems – Not just canned responses: reasons about user model and dialog context Target population: Heart failure patients following self-care guidelines – Collect information relevant to condition – Interpret with respect to self-care guidelines – Suggest appropriate course of action – Facilitate information sharing with doctors & family

10. Assisted Cognition AI + pervasive computing + assistive technology Potential users – Alzheimer’s disease – Traumatic brain injury – Autism Example applications – Maintaining a daily schedule – Indoor and outdoor navigation – Step-by-step task prompting – Behavior self-regulation

11. ACCESS Help persons with cognitive disabilities travel safely in their community and employ public transit – Huge issue for quality of life for millions of people GPS cell phone-based system – User carries phone during daily routine E.g. with job coach or family member – Automatically learns pattern of behavior Infers public transportation use – System notes breaks from ordinary routine Provides proactive help

12. Integrated Cueing & Sensing PEAT: handheld-based activity cueing system for persons with executive function impairment Problem: requires frequent input from user Solution: use sensor to detect activities – Reduce user interaction – Reduce “learned dependency” – Enable context-dependent cues Video Clip: Compliance rule – “Use cane when leaving house”