Howard Wactlar III PI Meeting, April 2010

1. Computer Science and Healthcare Synergy Howard WactlarIII PI Meeting, April 2010 Carnegie Mellon University Pittsburgh, USA

2. Lessons (being) Learned Collaborating with Medical Practitioners

3. Consider where in the research spectrum Research Inspiration YES Quest For Fundamental Understanding NO Consideration of use NO YES • Quadrant model of scientific research (Stokes) • One person’s basic research can be another person’s application Use-inspired Basic Research Pasteur Pure Basic Research Bohr Understand and control the processes Organizational Data collection Taxonomies Pure Applied Research Edison

4. Different challenges from the same data Automating the detection of behavioral & psychological symptoms of dementia Computer Scientists: • What are the health care appli-cations of machine understanding of video-based data? • How well can we identify & track individuals in real-world settings? • How do we automate the recog-nition of activities, behaviors and social interactions? • How can we reduce and mine the data so as to give healthcare providers summaries of relevant clinical events? • How do we protect subjects’ privacy and confidentiality? • How do we develop continuous capture and real-time processing capabilities? Geriatric Psychiatrists: • How do we overcome?: • Poor documentation • Unreliable, uninformed informants • Biased reporting • Cross-sectional observations • How can we diagnose early and accurately? • How can we assess the safety and efficacy of treatment interventions? • How can we assess the implementation of those recommendations? • Evidence Based Medicine

5. CareMedia: What are the observables? • Who? • Identify people across cameras, days. • What are they doing? • Wandering around • Socially interacting • Looking for things • Eating, sleeping in public • How well did they do it? • Quantify normal performance / measure change • Detect/report anomalies Click Here

6. Labeling Complex Motions and Sequences • Walking • Approaching • Standing • Talking • Hugging • Hand touch body normally • Shaking hands • Walking (moving) together • Hand in hand Enable audio / Click Here

7. Measure performance relevant to both disciplines Automated recognition performance – for CS researchers Determine a domain to impact a documented problem – for Medical researchers

8. Operational Definition of Aggression “An overt act, involving the delivery of noxious stimuli to (but not necessarily aimed at) another object, organism or self, which is clearly not accidental.”Patel & Hope, Acta Psychiatr Scand 1992;85:131-135 AB = aggressive behavior PAB = physically aggressive behavior VAB = verbally aggressive behavior Examples: spitting, grabbing, banging, pinching/squeezing, punching, elbowing, slapping, tackling, using object as a weapon, taking from others, kicking, scratching, throwing, knocking over, pushing, pulling/tugging, biting, hurting self, obscene gesture, and physically refusing care or activities .

9. Attempted Punch

10. Hair Pulling

11. The top ten retrievalresults have an 80%accuracy, which is muchbetter than the randomaccuracy 36.2% Results of Aggression Recognition

12. The Healthcare Crisis

13. The Good News

14. The Good News

15. couple The Good News

16. ! Population shift is coming, like it or not Percent of US population 70 and older: UNITED STATES: 2000 9% 80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 MALE FEMALE 35-39 30-34 25-29 20-24 15-19 10-14 5-9 0-4 12 10 8 6 4 2 0 14 14 Population (in millions) Source: US Census Bureau, International database

17. ! 0 2 4 6 8 10 12 14 Population shift is coming, like it or not Percent of US population 70 and older: UNITED STATES: 2050 16% 80+ 20.4 75-79 70-74 65-69 MALE FEMALE 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5-9 0-4 Population (in millions) Source: US Census Bureau, International database

18. The Healthcare Crisis • The most rapidly increasing age cohort is 85 and above. • Nearly half of persons over age 85 have Alzheimer’s disease • Disease prevalence with age > 85 years • Nursing home 20% • Incontinence 30% • Depression 10% • Parkinson’s < 10% • Comorbidity • 80% have > 1 chronic condition • 50% have > 2 chronic conditions • 25% have > 3 chronic conditions • For those >65, 30% of hospital admissions are due to medication non-compliance • By 2030, 1 in 2 working adults will be an informal caregiver • This year the U.S. will graduate only 238 primary care physicians

19. The Healthcare Crisis (2) • Its not just a cost crisis, it’s a capacity crisis • The challenge for science and technology is to enable a change in the healthcare delivery paradigm • Home-centered healthcare: Move the care away from the hospital /nursing home and the doctor / caregiver to the home and the individual (+ partner) + technology • This is not doing medicine. This is: • Sensing Data collecting & securing • Networking  Information gathering & annotating • Data mining Correlating, summarizing & reporting • Predicting Behavior modification • Machine learning Device actuating

20. The Healthcare Crisis (3) • Let’s restate this as a challenge: • Move ¼ of institutional care to the home in 10 years • Consider that as an appropriate III, HCC, and RI challenge

21. Thank You Questions ? Howard WactlarIII PI Meeting, April 2010 Carnegie Mellon University Pittsburgh, USA