Post on 22-May-2020
The impact of Big Data and AI on your Health June 9th 2017, AMC, Amsterdam
© 2017 IBM Watson Health
© 2017 IBM Watson Health
Dr Nicky Hekster Technical Presales EMEA IBM Nederland BV
Johan Huizingalaan 765 1066 VH Amsterdam Mobile: +31620303371 n.s.hekster@nl.ibm.com
Introduction Speaker
© 2017 IBM Watson Health
(Potential) conflict of interest IBM Watson Health
For this conference relations with other companies or institutes
OIZ, member of subgroup Standardization Member of IHE Nederland
Sponsoring, compensation, fees or research grants with a commercial company
None
Disclosure
© 2017 IBM Watson Health
IBM in Healthcare and Lifesciences 1950 – 2016
1953, Heart-lung machine 1982, Eyelasering
1991, nanoMRI for viruses
2010, Sequencing the cacao genome
1966, Digitization
2007, 3D-Avatar 2008, Mayo Clinic Medical Imaging Lab
2011, Watson
2000, Blue Gene Supercomputer
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Technology + Health = Impact!
Please stand up!
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© 2017 IBM Watson Health
Contribution of different determinants to our health Source: http://www.eengezondernederland.nl/Heden_en_verleden/Determinanten
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0.3% 0.5%
Physical exercise Smoking Overweight Cholesterol Salt Alcohol Fish Saturated fat Vegetables Fruit
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Determinants of health – the holistic view During our lifetime
20%
10%
70%Clinical data Medical reports, claims, payments, medication history, lab results, episodic data, …
Exogenous data Behavioral, socio-economical, lifestyle, environmental, psychological, nutrition, exercise, metabolism, …, but also weather, traffic, world events, …
Genetics data Endogenous, proteomics, metabolomics, micro-arrays, microbiome, …
Sources: "The Relative Contribution of Multiple Determinants to Health Outcomes", Lauren McGover et al., Health Affairs, 33, no.2 (2014)
"J.M. McGinnis et al., “The Case for More Active Policy Attention to Health Promotion,” Health Affairs 21, no. 2 (2002):78–93
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The world of wearables, swallowbles, implantables, … Citizen/patient reported and generated data
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Apps, social media, trusted and validated Internet sources… Citizen, elderly, athletes, patient, care professional, ….
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• Medical information doubles every 3 years. By 2020 it is expected to double every quarter.
• 80% of the healthcare professionals spends at most 5 hrs/month to keep abreast of his domain.
• Only 55% of the knowledge doctors use is evidence based: 1 out of 5 diagnoses are wrong or incomplete.
Doctors are suffering from infobesity
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The Healthcare Industry is dealing with data overload
Capturing and using data enables new insights into populations and individualized care
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2016: Google DeepMind AlphaGo wins Go
Artificial Intelligence is cool, but in fact it is very old
1974- 1980: 1st AI “Winter”
1970s 1980s 1990s
1956: “Birth” of AI John McCarthy coins term artificial intelligence (AI) at Dartmouth Conference
1965: First Expert Systems Stanford team led by Ed Feigenbaum creates DENDRAL and MYCIN
1987- 1993: 2nd AI “Winter” 1950: Turing Test
Turing introduces way to test for intelligent behavior
1990s: AI on www AI-based extraction programs prevalent on the www
1997: Deep Blue IBM Deep Blue defeats World Chess Champion
2014: Facebook Recognize individuals
2005: Autonomous car Stanford-built autonomous car wins DARPA Grand Challenge
2014: The market changes IBM formation of Watson Group and Google acquisition of Nest Labs
2011: Watson IBM’s Watson competes and wins on Jeopardy!
1960s 1950s 2010… 2000s
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1. There is a huge amount of data
© 2017 IBM Watson Health 2. There are better algorithms
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3. We have faster processors (GPUs) and computers
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4. Programming has become much easier, developing apps faster
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So we have: Digital intelligence building blocks to extract knowledge and wisdom from the data
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History of IBM Watson
2011 Jeopardy!
Grand Challenge
Demonstration
2006 – 2010 Research Project
R&D
2011–2013 Internal
Startup Division
Market Validation
2014–present IBM
Watson Group
Commercialization
2015 IBM Watson Health
Industry Vertical Thomas J. Watson
(1874 – 1956)
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The Jeopardy! Watson in 2011
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IBM Watson Cognitive Technology Platform in the Cloud
• Understands natural language
• Reasons and evaluates
• Learns and adapts
• Understands and engages an individual
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Watson for Oncology
Watson Genomics
Advisor
Watson for Clinical
Trial Matching
Watson in Oncology
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Data-driven Analytics
Insights Predictive Models
Data
Personalized engagement, data-driven analytics, and Watson
Published Knowledge
Can be • Population Based or • Personal, Longitudinal
Personalized Advice
Hi, I’m Watson. I can help you
meet your health goals
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Personal diabetes coach on a smartphone Sugar.IQ - Powered by Watson
The app predicts a low bloodsugar event 3 hours in advance
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Apple ResearchKit and Apnea and Sleep • ResearchKit, an open source framework that, when combined with IBM's big
data analytics and Watson's scalable data-mining, predictive analytics and cognitive capabilities, will provide medical researchers with insights. better insight drawn from a diverse global population.
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Teva’s Patient Technology Platform (e-RespiClick / e-SpiroMax)
- Dose confirmation (inhaled flow) - Dosing date/time stamp - Peak Inhaled Flow (PIF)
SABA (rescue)
ICS (inhaled corticosteroids) + LABA (controllers)
Asthma Guardian
- Clinical Study on efficacy of solution on going
Leverage real-time inhaler use and lung function monitoring to personalize treatment and predict risk of exacerbations by aligning Teva technology with Watson Health Cloud capabilities
- Over-use SABA alerts - Under-use controller alerts - Location-based triggers - Lung health tracking
SABA/LABA = Short/Long Asthma Beta Agonist
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Some Healthcare and Life Sciences start-ups Powered by Watson
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Nutrino A Watson-powered app for expectant Moms-to-Be
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Identification of patients at-risk for psychosis
– 1 out of every 100 people between the ages of 14 and 27 is at high risk for psychosis.
– Language markers
– speech coherence, phrase length, use of determiner words to link phrases, …
– 100% correctly identified the at-risk patients who went on to develop psychosis.
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The Internet of the Body The cognitive hypervisor
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Crowd-sourced personalization of health and wellness
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What more? For example … deep learning and computer vision
• Radiology
• Cardiology
• Dermatology
• Ophthalmology
• …
Clinical Records
Knowledge
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Project Lucy – Watson in Africa
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https://www.ibm.com/watson/education/
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IBM Watson Twist
ibmchefwatson.com