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

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.

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)

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)

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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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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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.

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!