Computer Science and Healthcare Synergy Howard Wactlar III

Computer Science and Healthcare Synergy Howard Wactlar III PI Meeting, April 2010 Carnegie Mellon University Pittsburgh, USA

Lessons (being) Learned Collaborating with Medical Practitioners Copyright © 2010

Consider where in the research spectrum • Quadrant model of scientific research (Stokes) • One person’s basic research can be another person’s application Research Inspiration Consideration of use NO YES Pure Basic Research Bohr Quest For Fundamental Understanding NO Organizational Data collection Taxonomies Use-inspired Basic Research Understand control the processes Pasteur Pure Applied Research Edison Copyright © 2010

Different challenges from the same data Automating the detection of behavioral & psychological symptoms of dementia Computer Scientists: Geriatric Psychiatrists: • What are the health care applications of machine understanding of video-based data? • How well can we identify & track individuals in real-world settings? • How do we automate the recognition 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? • 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 Copyright © 2010

Care. Media: 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 Copyright © 2010

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 Copyright © 2010

Measure performance relevant to both disciplines Automated recognition performance – for CS researchers Training Set Test Set Recognition Rate False Alarms Passing 21 15 93% 4 Standing conversation 25 28 100% 7 Greeting 7 6 33% 2 Walking assistance 35 40 88% 4 Wheelchair pushing 5 4 75% 2 Encounter 59 65 94% 1 Interactions Determine a domain to impact a documented problem – for Medical researchers Copyright © 2010

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. Copyright © 2010

Attempted Punch Copyright © 2010

Hair Pulling Copyright © 2010

Results of Aggression Recognition • The top ten retrieval results have an 80% accuracy, which is much better than the random accuracy 36. 2% Copyright © 2010

The Healthcare Crisis Copyright © 2010

The Good News Copyright © 2010

The Good News Copyright © 2010

The Good News couple Copyright © 2010

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 35 -39 30 -34 25 -29 20 -24 15 -19 10 -14 5 -9 0 -4 MALE 14 12 10 8 6 4 2 0 Population (in millions) Source: US Census Bureau, International database 16 Entire contents © 2006 Forrester Research, Inc. All rights reserved. FEMALE 14

Population shift is coming, like it or not Percent of US population 70 and older: ! UNITED STATES: 2050 16% 80+ 75 -79 70 -74 65 -69 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 MALE 14 12 10 8 6 4 2 0 Population (in millions) Source: US Census Bureau, International database 17 Entire contents © 2006 Forrester Research, Inc. All rights reserved. 20. 4 FEMALE

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 Copyright © 2010

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 Networking Data mining Predicting Machine learning Data collecting & securing Information gathering & annotating Correlating, summarizing & reporting Behavior modification Device actuating Copyright © 2010

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 Copyright © 2010

Thank You Questions ? Howard Wactlar III PI Meeting, April 2010 Carnegie Mellon University Pittsburgh, USA