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Personalized Medicine Research at the University of Rochester
Henry KautzDepartment of Computer Science
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Personalized Medicine
Smart SensingSmart Sensing
Intelligent Information
Management
Intelligent Information
Management
Effective InterfacesEffective
Interfaces
Putting the patient at the center of their health system
Family & Friends
HealthcareProviders
Web
Repositories
HealthcareInstitutions
Researchers
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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
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Intelligent Information Management
• Longitudinal data– Personal baseline– Detect trends & deviations from norm
• Personal health records– Privacy– Sharing– Anonymous aggregation
• Patient-centered decision support
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Effective Interfaces
• Multimodal– GUI, touch, gesture, speech, …
• Mobile– Portable, networked, wearable
• Intuitive– Easy to learn, use, maintain
• Adaptive• Proactive
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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
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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
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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
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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
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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
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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
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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”