Analytics Studies in Health Care

Introduction Brian Fisher

bfisher@sfu.ca SFU Interactive Arts & Technology / Cognitive Science UBC Media & Graphics Interdisciplinary Centre

My Background

• BA Biology, Medical Biophysics at CWRU Med

• Scientific Programmer, Varian

• Ph.D Experimental Psychology, UCSC

Currently• SFU School of Interactive Arts and Technology

• Social Cognition and Interactive Expertise in Natural and Computational Environments (SCIENCE lab)

• UBC Media & Graphics Interdisciplinary Centre (MAGIC) • Enable non-tech units to do (and fund) tech projects • New ventures: Thoughtshare, Xing Xing • Not-for profits: New Ventures BC • Education: HCI grad cert, VA training

SCIENCElab Applications• Emergency Management

• Mobile analytics / sensor analytics • “Virtual EOC” visual analytic environment

• Aircraft Safety, Reliability • “Pair analytics” of complex quant and text data

• Healthcare Monitoring & Management • Complex data in health research (CFRI) • Public health monitoring & management (BC Injury

Research and Prevention Unit)

• Government policy • World Bank projects with Vicki Lemieux

Recent SCIENCElab people

• Dr. Richard Arias-Hernández • Dr. Linda Kaastra • Dr. Samar Al-Hajj • Nadya Calderón • Tera Marie Green • Ethan Soutar-Rau

1995 InfoVis Conference Keynote "Information Visualization: Wings for the Mind"

• Increasing the memory and processing resources available to users

• Reducing the search for information by using visual representations to enhance the detection of patterns

• Engaging perceptual inference operations • Using perceptual attention mechanisms for

monitoring • Supporting manipulation of information

(Card, 1995)

“To  me  that  looks  like  a  plate  of  spaghe1…  Your  users  are  bears”

A  re9red  police  officer  Microso>  Research/Na9onal  Visualiza9on  and  Analy9cs  Center  Workshop  on  Regional  

Preparedness,  2007

7

Lab Mascot

"Il n'existe pas une catégorie de sciences auxquelles on puisse donner le nom de sciences appliquées. Il y a la science et les applications de la science, liées entre elles comme le fruit à l'arbre qui l'a porté” ——— Louis Pasteur

Pure Basic Research (Bohr)

Use-inspired Basic

Research (Pasteur)

Sampling, Description, Taxonomy (Audubon)

Pure Applied Research (Edison)

Quest for Fundamental

Understanding?

No

Yes

Consideration of Use ?

No

Yes

(1822–95)

Pasteur’s Quadrant (Stokes)

How can science help vis?

• Design system in accordance with known visual cognition theories & phenomena

• Evaluate prototypes & systems with regard to theories of visual cognition:

• Iterate • “Close reading” of interface &/or video of interface use • Look for threats/abilities human Cognitive Architecture • Formulate hypothesis, do lab experiment on CA • Change design, evaluate in field study

Example: Visual Cognition in ATC• “Close reading”

NextGen ATC fishtank video

• Identify threat to human cognitive architecture

• Here: How will global motion cues affect FINSTs?

Liu, G. Austen, E. L., Booth, K.S. Fisher, B., Argue, R. Rempel, M.I., & Enns, J. (2005) Multiple Object Tracking Is Based On Scene, Not Retinal, Coordinates. Journal of Experimental Psychology: Human Perception and Performance. 31(2), Apr 2005, 235-247.

http://www.youtube.com/watch?v=tKJVB4id_TY

Impact of display motion on multiple object tracking

Tracking vs object speed

Origins of Visual Analytics

Tools support understanding implications of data ▪ Synthesize information & derive insight from massive, dynamic,

ambiguous, & conflicting data ▪ Detect the expected & discover the unexpected ▪ Build timely, defensible, & understandable assessments ▪ Communicate assessments effectively for action.

“The beginning of knowledge is the discovery of something we do not understand.” ~Frank Herbert (1920 - 1986)

Jim Thomas slide

“The science of analytical reasoning facilitated by interactive visual interfaces”

“A grand challenge for the scientific enterprise”

• Battelle/PNNL 2004 R&D Agenda panel • University: Brown, GMU, Georgia Tech, OSU, Penn State,

Purdue, SFU , Stanford, UC, UI, UM, UNC, UU, WPI • Industry: Boeing, Microsoft, PARC, Sandia Labs • Gov: CIA, DHS, FBI, NIST, NSA, unspecified

• National Visualization and Analytics Center • VA Industry Consortium • DHS University Center of Excellence in VA • IEEE VAST conference • NSF FODAVA • EU 7th Framework VisMaster Coordination Action

16

VA as a Translational Cognitive Science

• Lab: Directed basic research in cognition • Clinic: Analysis “in the wild”— what validates it? • Lab to Clinic translation

• Design & software engineering methods that incorporate scientific knowledge

• Field experiments to evaluate theory in context

• “Clinic” to “Lab” translation • Cognitive ethnography (Hutchins) ex: Emergency ops • “Close reading” of interface using cogsci theory • Pair Analysis field experiments w JAT video analysis

Research Methods• Cognitive ethnography

• Document current tools and methods • Understand “lab culture” e.g goals & reward structures

• “Pair analytics” with VA tools & datasets, “close reading” visualization & collaborative “joint activity” of analysis

• Collaborate with UNC Charlotte Vis Center on design and engineering of interactive Principle Component analysis (iPCA) tool

Ongoing Directed Basic Research

• Perceptual cognition (e.g. FINSTs & ATC) • Human Cognitive Models with Bill Ribarsky

and Daniel Keim • Personal Equation work (Po, Greensmith) to

capture innate and acquired differences • Eye movements & visual cognition in complex

visualization environments with Daniel Weiskopf and colleagues at Stuttgart

• “Pair Analysis” sessions designed for JAT video analysis • Student visual analysis (VAE)

& subject matter expert (SME) • VAE drives, expert navigates

Visual Analytics in Pair Analysis

Arias-Hernandez, R, Kaastra, L.T., and Fisher, B. (2011) Joint Action Theory and Pair Analytics: In-vivo Studies of Cognition and Social Interaction in Collaborative Visual Analytics. In L. Carlson, C. Hoelscher, and T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 3244-3249). Austin TX: Cognitive Science Society.

• Video analysts & capture screen • Video analysis focuses on understanding of

process analytics • Video analysis tools: Chronovis*, NVIVO, Atlas.ti

Joint Activity Theory (Clark)

• Communication as Joint Activity, like playing a duet or paddling a canoe

• H. H. Clark’s theory of joint activity • Defines kinds of common ground • Formalizes the notion of activity as a “joint

action” • Describes the processes by which common

ground is developed through joint action

• We extend Clark from F2F to focus on technology as integral to communication and collaborative analysis

Health research example: iPCA for Immunological

Response Analysis

Dr. Samar Al-Hajj

Reduce Child Mortality from Infectious Diseases

• UBC & BC Children’s Hospital (Dr. Kollmann), Stellenbosch Inst. for Advanced Studies • Morbidity/mortality in rural South African children • Effects of public health intervention e.g vaccination

• We build on UBC BioSci/CompSci student David Shih, Cogsys student Kevin Ho on work terms (Rensink)

• Goal is to understand factors that enable competent immune response to infection

Data: Flow cytometry (FlowJO)

• Compare 2 blood samples (e.g. before/after vaccine) • Look at 18 cytokines that reflect WBC activation x 3M

White Blood Cells • Percent positive for given cytokine •MFI (Mean Fluorescence Intensity) for activation

• 3 cell types •Monocytes •mDC • pDC

Construct: PolyFunctionality Degree (PFD)

• A measure of the degree to which a cell is polyfunctional, i.e. production of multiple cytokines

• Expressed in terms of percentage • Pfd1 = % 1 cytokine • Pfd2 = % 2 cytokines • Pfd3 = % 3+ cytokines

Visualization: Ternary Plots

X

• Does the increase in polyfunctionality in the adult simply reflect increased overall cytokine production, or is it a unique functional response the neonate is less capable of? PFD1 PFD2

PFD3+

Research Question

NA NA NA

Polyfunctionality Monocyte 3M-003 (TLR7/8) Dose Response neo < adult

Pair analysis w iPCA

Conclusions & future work

• Best Student Discovery Exhibition Award (IEEE VAST 2011)

• iPCA approach shows promise • iPCA needs cognitive and perceptual skill

training to enable health professionals to synthesis meaningful information.

• Future collaboration depends on • Buy-in of health science researchers and technology

developers • Appreciation of the need for science-informed design

and evaluation

Public Health Example: Child Injury Prevention

Samar Al-Hajj SIAT Ph.D. student

Al-Hajj, S., Pike, I., Riecke, B., & Fisher, B. (2013) Visual Analytics for Public Health: Supporting Knowledge Construction and Decision-Making (full paper). Proceedings of the 46th Annual Hawaii International Conference on System Sciences. IEEE Digital Library

Collaborative Visual Analytics for Public Health: Facilitating Problem Solving and Supporting Decision-Making (2014 SFU Doctoral dissertation)

Dr. Samar Al-Hajj Dr. Ian Pike UBC Pediatrics

BC Child Injury Project• Apply VA methods to multi-stakeholder

exploratory analysis of injury indicator data. • Data are complex and heterogeneous (patients’ age,

sex, socioeconomic status as well as injuries’ types and geographic locations).

• Visualizations created using Tableau Software.

• Build & evaluate collaborative VA to facilitate insight generation, knowledge construction and decision-making.

Interactive Visual Analytics Dashboard System

Methods• 6 Analytical Sessions: Visual Analysis and

Subject Matter Experts Pair Analysis • 1 Group Analysis Session: VAE, multiple SME,

Process facilitator • JAT analysis • Questionnaire document perceived utility &

usability of tech and process • Goal is to develop and evaluate a socio-

technical system for making group decisions that are informed by data

Study designGroup Analytics (GA): Pair Analytics + Delphi Method

Analysis of sessions

24

Challenges• Software engineering challenges • Funding challenges – NSERC or CIHR? • Understanding cultures

• The laboratory • The larger research community

• Interdisciplinary research management • Responsibility for the project? • Managing expectations • Insuring research products for all

What we need to succeed

Success in big-tent visual analytics will depend on ongoing collaboration on a set of related projects in application-focused institutes, supported by an interdisciplinary community of visual analytics researchers.

New partnership

The PMI is incorporated as a not-for-profit Canadian company based in Vancouver, BC Board of Directors: Pieter Cullis (Co-Chair), David Huntsman (Co-Chair), Bruce McManus, Jim Russell and Martin Dawes COO: Rob Fraser

PMI Vision: Introduce an individualized approach to preventive and curative healthcare based on the molecular makeup of the individual and their disease.

The PMI Group of Companies

PBI

1) GenXys: PGx to guide prescription practices in family practice ▪ Principals: M. Dawes ▪ Status: Operating

2) Contextual Genomics: Cancer gene analysis to guide chemotherapy ▪ Principals: D. Huntsman, C. Wagner ▪ Status: Operating

3) Cyon Therapeutics: Treatments for septic shock ▪ Principals: J. Russell ▪ Status: Operating

4) Personalized Biomarkers Inc: Omics to guide diabetes therapy ▪ Principals: T. Elliot ▪ Status: Seed

5) Microbiome Insights: Microbiome analyses ▪ Principals: B. Finlay, B. Mohn, M. Kendall ▪ Status: Seed, but operational

6) Molecular You Corporation: Longitudinal Omics Analysis ▪ Principals: P. Cullis, R. Fraser ▪ Seed, not operational

Engaging the PMI

• Work on existing projects, collaborate w UBC, ECUAD, others: what is our best role?

• Focus on scalability, system integration • Engage PMI in personalized wellness and

health promotion, opportunities for tech development

• Consider environmental determinants of health and wellness with Canadian Environmental Health Atlas Bruce Lanphear

Acknowledgements

45