Patient Powered Research with Big Data and Connected Communities by Assoc. Prof. Christopher Yang

WKWSCI

SYMPOSIUM

2014 Big Data, Big Ideas for Smarter Communities

Patient Powered Research with Big Data and Connected Communities

Chris Yang College of Computing and Informatics

Drexel University

• Healthcare is transforming Reactive Care Proactive and Preventive care

Episodic Response Continuous Well-being Monitoring

Clinic-centric practice Patient-centered practice

Training-based Interventions Globally Aggregated Evidence

• Patients are playing active role in maintaining their health

WKWSCI SYMPOSIUM 2014 Big Data, Big Ideas for Smarter Communities

Social Media

• With the advance of Web 2.0 technologies, the Web is not only a resource that patients look for health information but also a virtual community where patients interact with each other to provide different kinds of social support and exchange information where they may not be able to obtain from local health professionals.

“e-patients are looking for ‘just-in-time someone-like-me” health information”

- Boase et al, 2006; Fox & Jones, 2009

*E-patient: internet user that is looking for health information

“e-patients are looking for a sense of community”

-Wright & Bell, 2003

72% read patient blog, health newsgroup, or health website

39% used social

networking site

26% shared or viewed

updates



Integrate with Leading Health Devices

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• We are now utilizing diverse data to provide automated and augmented insight, discovery, and evidence-based health and wellness decision support

• a cyber-based empowering of patients and healthy individuals that enables them to play a substantial role in their own health and treatment


Forum

Journal

Notes

http://dunx1.irt.drexel.edu/~kyc24/asist2010fig1.jpg

WKWSCI

SYMPOSIUM

2014 Big Data, Big Ideas for Smarter Communities

Informational Support Offered

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

Referral Advice Opinion Personal Fact

1. K. Chuang and C. C. Yang, “A Study of Social Positions In an Online Alcoholism Community,” Proceedings of International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, Washington, D.C., April 2 – 5, 2013.

2. K. Chuang and C. C. Yang, “Interaction Patterns of Nurturant Support Exchanged in Online Health Social Networking,” Journal of Medical Internet Research, vol. 14, no.3, 2012.

3. K. Chuang and C. C. Yang, “ A Study of Informational Support Exchanges in MedHelp Alcoholism Community,” Proceedings of International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction , College Park, MD, April 3-5, 2012.

4. K. Chuang and C. C. Yang, “Helping You to Help Me: Exploring Supportive Interaction in Online Health Community,” Proceedings of ASIS&T 2010, Pittsburgh, PA, October 22-27, 2010.

5. K. Chuang and C. C. Yang, “Social Support in Online Healthcare Social Network,” Proceedings of iConference, Urbana-Champaign, IL, February 3-6, 2010.

Referral refers the recipient to a resource

Advice suggests actions to deal with situation

Opinion a view or judgment formed about something, not necessarily based on fact or knowledge

Personal experience

stories about person’s experiences or incidents as a way of presenting information.

Fact reassesses the situation and presents facts.

Nurturant Support Offered

Esteem Gives positive comments validating a recipient’s self-concept, alleviating feelings of guilt as a person; includes compliments, validation, and relief from blame

Networking Focuses on messages to broaden support seekers’ social network so they don’t feel alone, by connecting them to others with similar situations; includes access, presence, and companions

Emotional Gives expressions that support recipient’s feelings or reciprocates emotion; the emphasis of this category is on supporting emotional states rather than the recipient’s identity or self-concept 0%

10%

20%

30%

40%

50%

60%

Esteem Networking Emotional


11

Drug Safety Signal Detection

• Adverse drug reactions (ADRs) are causing a substantial amount of hospital admissions and deaths – 5.3% of hospital admission were

associated with ADRs

– 7% of medical errors were attributed to ADRs

• Pre-marketing review process cannot possibly identify all potential adverse effects.

• Post-marketing surveillance programs must be conducted to identify new adverse effects and evaluate their potential impact on the public health


12

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Precision Recall F-measure

Associations Mining-Lift

Treato

SideEffective

AdverseEvents


Healthcare Heterogeneous Network Visualization and Mining

13 WKWSCI SYMPOSIUM 2014

Big Data, Big Ideas for Smarter Communities

Drug-Drug Interaction Detection

• Drug-drug interactions (DDIs)

– a significant drug safety problem for health consumers

– may account for up to 30% of unexpected adverse drug reactions

– often caused by shared pathways of metabolism or interesting pathways of drug action

– DDIs detection is of great clinical importance because most interactions could result in precaution of prescription, absolute contraindications of combination use, or even drug withdrawal from market





• Healthcare social media empowered patients to connect with other patients and health professionals

– Exchange information as well as nurturant support

– A new platform health intervention

• Big data for knowledge discovery to supplement the traditional clinical approach


Education

Patient Powered Research with Big Data and Connected Communities by Assoc. Prof. Christopher Yang