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Ontology-based Deep Learning for Human Behavior Prediction in Health Social Networks
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Transcript of Ontology-based Deep Learning for Human Behavior Prediction in Health Social Networks
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The 2015 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
Ontology-based Deep Learning for Human Behavior Prediction
in Health Social NetworksNhatHai Phan1, Dejing Dou1, Hao Wang1, Brigitte Piniewski2,
and David Kil3
1 Computer and Information Science Department, University of Oregon, Eugene, OR, USA
2 PeaceHealth Laboratories, Vancouver, WA, USA3 HealthMantic Inc. Los Altos, CA, USA
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Outline
• Overweight/Obesity, YesiWell Health Social Network
• Motivation of the SMASH project• Human Behavior Prediction– Ontology-based Restricted Boltzmann Machine
• Experimental Results • Conclusions and Future Works
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Obesity and Physical Activity Interventions
• 18 states (30% - <35%), 2 states (>= 35%)• Medical cost:
– $147 billion in 2008
• 30 minutes, 5 days• Interventions
– Telephone (16)– Website (15)– Effective in
short term
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Prevalence* of Self-Reported Obesity Among U.S. Adults
CDC, http://www.cdc.gov/obesity/data/prevalence-maps.html-2014
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Overweight and Obesity
• The prevalence of Obesity has increased from 23% to 31% over the recent past, and 66% of adults are overweight. Why?
• It cannot be explained only by genetics and has occurred among all socioeconomic groups. Weight gain in one person might influence weight gain in others.
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12,067 people for 32 years (Christakis-Fowler 2007)
N. Christakis and J. Fowler. “The spread of obesity in a large social network over 32 years.” New England Journal of Medicine, 357(4), 2007. 5
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Motivation of Our Research
• Can healthy behaviors, e.g., physical exercise, also spread in the social networks?
• Can we design a social network to help the spread of healthy behaviors better?
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YesiWell Health Social Network (2010-2011)
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Clear Correlations (254 users)
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SPD (Steps per Day) vs. SN size
The larger your social network, the more active you are?
SN size
SPD
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Impact of Online Social Network
• Increase weekly leisure walking from 129 to 341 minutes, on average, a 164% increase over the 6-month study period, compared with a 47% increase for the control group (i.e., ~250 non-users).J. Greene, R. Sacks, B. Piniewski, D. Kil, and J.S. Hahn "The Impact of an Online Social Network
with Wireless Monitoring Devices on Physical Activity and Weight Loss." Journal of Primary Care and Community Health, 4(3): 189-194, 2013.
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Semantic Mining of Activity, Social, and Health Data (NIH/NIGMS Funded in 2013, R01 Grant) (PI: Dou)
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Research Aims
• Understand key factors that enable spread of healthy behaviors in a social network. (ICDM’12, CIKM’14, ASONAM’15)
• Develop Formal Semantic Web Ontologies for Healthcare Social Networks. (BCB’15)
• Identify social network structures that maximally enable spreading of wellness with recommendations. (CIKM’14)
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Detected Communities for Influence Propagation (CIKM’14)
• Influencers: circle nodes• Influenced users: rectangle nodes• Non-Influenced users: triangle nodes
12N. Phan , D. Dou, X. Xiao, B. Piniewski, and D. Kil, “Analysis of physical activity propagation in a health social network,” in CIKM’14, pp. 1329–1338.
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Outline
• YesiWell Health Social Network and SMASH• Motivation of the SMASH project• Human Behavior Prediction– Ontology-based Restricted Boltzmann Machine
• Experimental Results • Conclusions and Future Works
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Human Behavior Prediction
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t1t 1tDecrease exercise Increase exercise
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Dataset, Features, and Task• YesiWell dataset
– 254 users– Oct 2010 – Aug 2011
• BMI • Wellness score
• Prediction Task: Try to predict whether a YesiWell user will increase or decrease exercises in the next week compared with the current week.
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cHbAULDLUHDLTGUBMIUy
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Challenging for Existing Models
Existing Models for Prediction• Logistic Auto-Regression (LAR),
Socialized Logistic Auto-Regression (SLAR), Behavior Pattern Search (BPS), Gaussian Process Model (GP), Socialized Gaussian Process (SGP, ICDM’12)
Challenges for Existing Models• Unobserved (hidden) social
relationships/events• Evolving of the social network
– Temporal effects• (Explicit & implicit) Social influences
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Social Restricted Boltzmann Machine (ASONAM’15)t
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Self-Motivation
Implicit Social Influence
Environmental Events
Explicit Social Influence
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N. Phan , D. Dou, B. Piniewski, and D. Kil, “Social Restricted Boltzmann Machine: Human Behavior Prediction in Health Social Networks ,” in ASONAM’15.
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Biomedical Ontologies• The Gene Ontology (GO): to standardize the formal representation of
gene and gene product attributes across all species and gene databases (e.g., zebrafish, mouse) – Classes: cellular component, molecular function, biological process, … – Properties: is_a, part_of
• UMLS, SNOMED CT, ICD-9/10/11, NDFRT: comprehensive dictionaries and ontologies for medical terms, diseases, and drugs.
• The National Center of Biomedical Ontology (NCBO) at Stanford University– >300 ontologies (hundreds to thousands concepts each one) 4 millions of
mappings.
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SMASH Ontology and Its Hidden Variables
• Ontology development– Biomarkers: a collection of
biomedical indicators health conditions
– Social Activities: a set of interactions between social entities, either persons or social communities
– Physical Activities: any bodily activity involved in daily life.
• Represent concepts by – their own properties – the properties of its related
concepts– the representation of their sub-
concepts
http://bioportal.bioontology.org/ontologies/SMASH NCBO BioPortal
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Ontology-based Restricted Boltzmann Machine (ORBM)
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ORBM Model Learning
• Minimize the energy function
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Performance of the ORBM Model
• ORBM: 85.9%, and SRBM: 83%• Previous State-of-the-art: 75.21% (the SGP model
in our ICDM’12 work)Y. Shen, R. Jin, D. Dou, N. Chowdhury, J. Sun, B. Piniewski, and D. Kil. “Socialized gaussian process model for humanbehavior prediction in a health social network.” In ICDM’12, pages 1110–1115.
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Synthetic Health Social Network
• Pajek to generate graphs under the Scale-Free/Power Law Model– 254 nodes, and the average node degree is 5.4
• Map pair-wise vertices between the synthetic social network and the YesiWell health social network– by applying PATH
M. Zaslavskiy, F. Bach, and J.-P. Vert. “A path following algorithm for graph matching.” In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 5099, pages 329–337, 2008.
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Performance of ORBM on the Synthetic Data
• ORBM model also outperforms state-of-the-art models
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Conclusions and Future Works• Propose Ontology-based Restricted Boltzmann Machine
(ORBM) model– Self-motivation, social influence, environmental events– Extend traditional deep learning framework to ontology-based
deep learning• Human behavior prediction
– ORBM: 85.9%, SRBM: 83%– Previous State-of-the-art: 75.21%
• The ORBM model can be applied on different datasets• Try other Deep Learning models like CNN, etc. in the near
future works
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The 2015 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
{haiphan, dou}@cs.uoregon.eduSMASH Project: http://aimlab.cs.uoregon.edu/smash/
YesiWell Health Social Network
Thank you!