"Data-Driven Healthcare", Alejandro (Alex) Jaimes, CTO & Chief Scientist at Acesio
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About the Author:
Alejandro (Alex) Jaimes is CTO & Chief Scientist at Acesio. Acesio focuses on Big Data for predictive analytics in Healthcare to tackle disease at worldwide scale, impacting individuals and entire populations. We use Artificial Intelligence to collect and analyze vast quantities of data to track and predict disease in ways that have never been done before- leveraging environmental variables, population movements, sensor data, and the web. Prior to joining Acesio, Alex was CTO at AiCure and prior to that he was Director of Research/Video Product at Yahoo where he led research and contributions to Yahoo's video products, managing teams of scientists and engineers in New York City, Sunnyvale, Bangalore, and Barcelona. His work focuses on Machine Learning, mixing qualitative and quantitative methods to gain insights on user behavior for product innovation. He has published widely in the top-tier conferences (KDD, WWW, RecSys, CVPR, ACM Multimedia, etc), has been a visiting professor (KAIST), and is a frequent speaker at international academic and industry events. He is a scientist and innovator with 15+ years of international experience in research leading to product impact (Yahoo, KAIST, Telefonica, IDIAP-EPFL, Fuji Xerox, IBM, Siemens, and AT&T Bell Labs). He has worked in the USA, Japan, Chile, Switzerland, Spain, and South Korea, and holds a Ph.D. from Columbia University.
3. A Global Problem
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● 12.6 Million people died from living in unhealthy environments in 2012
● 1 in 4 global deaths
4. A Global Problem
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● 12.6 Million people died from living in unhealthy environments in 2012
● 1 in 4 global deaths
5. A Global Problem
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● 12.6 Million people died from living in unhealthy environments in 2012
● 1 in 4 global deaths
6. Healthcare is Based on Data (and models)
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● Medical Doctors undergo lengthy training
● Typical doctor visit
•Data collected
•Model matching
•Diagnosis
•Action
12. ● the social and economic
environment,
● the physical environment, and
● the person’s individual
characteristics and behaviours.
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WHO: Determinants of Health
13. ● Income and social status - higher income & social status, better health.
● Education – low education levels are linked with poor health, more stress
and lower self-confidence.
● Physical environment – safe water and clean air, healthy workplaces,
safe houses, communities and roads all contribute to good health.
● Employment and working conditions – people in employment are
healthier, particularly those who have more control over their working
conditions
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WHO: Determinants of Health
14. ● Social support networks – greater support from families, friends and
communities is linked to better health. Culture - customs and traditions,
and the beliefs of the family and community all affect health.
● Personal behaviour and coping skills – balanced eating, keeping
active, smoking, drinking, and how we deal with life’s stresses and
challenges all affect health.
● Health services - access and use of services that prevent and treat
disease influences health
● Genetics, gender, ..
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WHO: Determinants of Health
15. ● Transport
● Food and Agriculture
● Housing
● Waste
● Energy
● Industry
● Urbanization
● Water
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WHO: Evidence Base of Health Determinants
16. ● Transport
● Food and Agriculture
● Housing
● Waste
● Energy
● Industry
● Urbanization
● Water
16
The World is Increasingly Connected
19. Environmental Public Health: Overview
PublicHealth Behavioral Science/
Health Education
Biostatistics
Environmental Health
Epidemiology
Health Services
Administration
The branch of public health that focuses on both the natural
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21. Creating Value with Machine Learning
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PHARMACEUTICAL CLINICAL
CLAIMS & COST PATIENT BEHAVIOR
Improved Disease Detection and Classification
Earlier Insights in Disease Progression
Efficient Triaging
Population Health Management
Better Health Outcomes
Health Expense Savings
29. PATIENT DATA
Environmental Public Health Challenge
Data Silos | Data Duplication | Unidirectional Information | Static Insights
PHARMACEUTICA
L
CLINICAL CLAIMS & COST PATIENT BEHAVIOR
Example Data Factors
• Drug Exposure/
History
• Clinical Trials
Example Data Factors
• Genetics
• Medical Imaging
• EMR: Medical History
Example Data Factors
• Utilization of Care
• Cost Estimates
Example Data Factors
• Lifestyle
• Social networks
• Socio-economic
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