Team Science for Precision Medicine:

The Utah PRISMS Informatics Center

Professors Kathy Sward and Julio C. Facelli

2016 Air Quality, Health and Society Symposium,

September 21st 2016

Center for Clinical &

Translational Science

Genome

Exposome (t)

Phenotype (t)

Environmental Data

Educational Data

Socioeconomically Data

Sensors

Interment of Things

…..

Precision Medicine Framework

Exposome – Emerging concept

– Total set of environmental factors to which a person is

exposed, including the complex interplay between

environmental factors and between environmental, behavioral,

& psychosocial factors

– Influence health and diseases modifying the genome

http://www.niehs.nih.gov/research/supported/dert/programs/peph/podcasts/exposome/

Exposome

• the total set of environmental

factors to which a person is

exposed

– the complex interplay between

environmental factors

– and between environmental, behavioral

& psychosocial / socioeconomic factors;

– that in turn influence health and disease

• Encompasses life-course of

exposures from prenatal period

onwards.

• Complements genome by providing

a comprehensive description of

lifelong exposure history.

General External Environment

Specific External

Environment Internal

Environment

Overlapping domains within exposome

5

Exposomics • Study of defining, generating and utilizing exposomes in biomedical

research.

• Ongoing efforts: – HELIX: Early life exposome

– EXPOsOMICS: Assess exposures

– HEALS: Studies exposure to environmental stressors and health outcomes

– NIH’s Environmental influences on Child Health Outcomes (ECHO) Program. Understanding the effects of environmental exposures on child health and development

• Requires a systems biology approach. Body's responses to environmental influences including endogenous metabolic processes that can alter or process the chemicals to which humans are exposed.

• ‘Expotying’: Exposure of a biological entity usually with reference to a specific characteristic under consideration. Also called as Exposome Informatics, Exposure Information Science.

• Provides great opportunities to Biomedical Informatics

Utah

• Inversions – days of good air vs bad air

• Lots of complementary research

– Environmental/air quality

– Sensors

– Pediatric asthma

– Informatics

• High level of interest in the community

• Technical infrastructure is robust

• Robust “knowledge infrastructure” (expertise)

http://utahpoliticalcapitol.com/wp-content/uploads/2013/04/inversion01.jpg

Posssible Sensors

• Goal: Use commercial wireless devices (IoT / smart

home)

• Air: PM, O3, NO2, SO2, CO, ..., VOC, temp, humidity

• Phone: position, INS (acc, gyro, mag), noise, light

• Wearable: Heart rate, INS, activity

• Sleep: respiration rate, heart rate, movement, duration

• Home: door/window, occupant locations, elec appliance usage,

furnace/ac control, video, audio

Variety: What is measured

• Particulate Matter

• PM2.5 ,PM10

• Small particles, large particles

• Some categorize more

granularly – suspended

particulate matter, respirable

particles, coarse/fine/ultrafine

– some separately categorize

“soot” https://en.wikipedia.org/wiki/Particulates

PRISMS Challenges

PRISMS program research will need:

The Utah PRISMS Informatics Center

Center for Clinical &

Translational Science

Team Leadership

• PD: Sward, Facelli

• CoN: College of Nursing; BMI: Department of Biomedical Informatics,

School of Medicine; CCTS: Center for Clinical and Translational Science

• Project manager: Heather Oldroyd (Pediatrics)

CoN, BMI BMI, CCTS

Admin core: project oversight, administration & reporting, evaluation,

coordination. Cross-project & “all hands” meetings.

Project 1: Sensor interface and

subject interaction

• Team

– Leads: Patwari, Meyer

– Key Collaborators: Collingwood, Kim

Electrical & Computer

Engineering Scientific Computing

& Imaging

Pediatrics Bioengineering

P1: Specific Work Areas

– Data pipeline, aka “what happens in the home”

– Bi-directional – gathering data from sensors, presenting

information to parents and kids

– Mobile apps, sensor communications, web portal, age-

appropriate messaging

Project 1 Status • We have a fully functional (version 1.0)

hardware and software system

– collects data from wireless sensors to a

raspberry pi-based device

– sends data to a cloud based server using

the home’s broadband internet

– where it can be visualized by the researcher.

Project 1 Status • The configuration of the gateway is automated

via a script and a configuration file which can

be edited to meet researcher specifications

18

Dylos DC1100 Pro Air

Quality Monitor

Web Interface showing a graph of a single day

during a home deployment Web Interface showing a pie chart for

daily activities for Monitor x

Air Quality Application showing events of

air quality activities on a smart device

Home Smart Device

Air Server

Project 1 Deployments

• Deployed system in two homes for 3-4

months each.

– Debugging, performance testing,

examination of data quality

• We conducted experiments regarding

calibration to increase data reliability and

reduce variability between sensors

Deployment B: Time Series of PM2.5 & Events

Dustin

g

Cleaning

w/

cleaning

solution

Vacuuming

Many

people in

the room

All 11 sensors in one room: 6 of them plotted here

Grafana: Graph and dashboard builder for visualizing time series metrics

Prototype data visualization

Project 2: Informatics architecture

(Federation-Integration Platform)

• Team

– Leads: Gouripeddi, Facelli

– Technical lead: Madsen

– Key Collaborators: Kelly, Horel

BMI, CCTS CCTS

Chemical

Engineering Atmospheric

Sciences

Project 2: Specific Work Areas

• Core infrastructure

• Builds on and extends work from the CCTS

OpenFurther architecture

• Model driven, standards based, open source

• Makes digital objects FAIR (Findable, Accessible,

Interoperable, Reusable) – OpenFurther has FAIR as its main requirement and has been so since its

inception

Project 2: Specific Work Areas

– Sensor Data Harmonization Framework

• Integration and Federation of data from multiple sensors, sensors &

clinical/research data

• logical data model to store and harmonize metadata from environmental

sensors

– Sensor - Environmental Mathematical Modeling.

• High resolution spatial-temporal grid along with uncertainties.

– Data integration and interim storage.

• Integrate mathematically modelled data with collected data.

• Preprocessing before passing information on to the DCC

Challenges and Informatics

Methods and Solutions Data Sources

Mathematical Modeling

Uncertainty Characterization

Data Integration

• Semantics

• Metadata

• Time & Event Modeling

• Infrastructure for multi-scale, multi-omics integration

Presentation/Visualization

Opportunities

• Leverage CTSA biomedical informatics

core

• While the PRISMS emphasis is currently

on the incorporation of data from mobile

and stationary sensors, our infrastructure

will be adaptable and scalable to other

emergent measurement modalities that

are becoming part of the “internet of

things”.

Semantics for Data Integration

• Stored in Terminology/Ontology Server

• Examples – Semantic Sensor Network Ontology: Describes sensors and

observations, and related concepts.

– Sensor Model Language (SensorML): Standard models and XML schema for describing sensors systems and processes associated with sensor observations.

– PhenX Phenotypic Terms: Standard measures related to complex diseases, phenotypic traits and environmental exposures.

– Exposure ontology (ExO): Facilitate centralization and integration of exposure data to inform understanding of environmental health.

– Standard biomedical ontologies and terminologies: Gene Ontology, UniProt, SNOMED

Metadata • Stored in Metadata

Repository

• Relational or graph stores

• Stores

– Source and Central Data

Models

• Harmonized sensor

data model

– Data provenance and

associated uncertainty

– Inter-model

transformative functions

Air Quality Mathematical Models

•Validated on the east coast

•Doesn’t consider Altitude

•12 kilometer resolution

•Hierarchical Bayesian model

Environmental Protection Agency – Center for Disease

Control Model

•Describe regional and small-scale spatial and temporal gradients

•Uses measured PM concentrations, monitoring site location, GIS-based location-specific characteristics and location-and month-specific meteorological data, and spatial smoothing of monthly and long-term averages

Generalized Additive Mixed

Models

McMillan, Nancy J., David M. Holland, Michele Morara, and Jingyu Feng. “Combining Numerical Model Output and Particulate Data Using Bayesian Space–time Modeling.”

Environmetrics 21, no. 1 (February 1, 2010): 48–65. doi:10.1002/env.984.

Yanosky, Jeff D., Christopher J. Paciorek, Francine Laden, Jaime E. Hart, Robin C. Puett, Duanping Liao, and Helen H. Suh. “Spatio-Temporal Modeling of Particulate Air Pollution in

the Conterminous United States Using Geographic and Meteorological Predictors.” Environmental Health 13, no. 1 (August 5, 2014): 63. doi:10.1186/1476-069X-13-63.

• Fill gaps in measured data with mathematically models

• A library of AQ data models to provide high spatio-temporal resolution with a framework validate the model output.

Uncertainty Quantification

PRISMS Federated Data Integration Architecture

Study Generation Tools

Experiment

Parameters

Code for

devices

(if needed)

Code for

gateway

Code for

phone

app

Code for

server

Study

Design

Sensors, Data Rates,

Subject Feedback,

Subject Surveys

New types of studies

Behavior, Sensing, and Feedback

Two (complementary) options for

feedback:

1. Based on raw data

2. Based on (estimated) activities

– inferred from sensor data

Feedback:

Data, Alerts

Behavior,

Activities

Sensor

Data

Analysis

Project 3: Research Platform

• Team

– Leads: Sward, Nkoy

– Key Collaborators: Stone, Cummins, Wong, Meyer

(Facelli)

Pediatrics, BMI

Pediatrics CoN, BMI CoN

CoN

Project 3: Specific Work Areas

Help asthma researchers to conduct studies

– Requirements and use cases

– Sensor library

– Research platform.

• View into the sensor library - for researchers, parents, sensor

developers. What does each group want/need to know?

• Data visualization, big data analytics

– End to End testing

Project 3: Perspective

• Usability, ease of use. Work with projects 1 and 2

for iterative user-centered design approach

• Parent/child advocacy

– What do parents want to know before participating in this

sort of research?

• We will develop ways to link data from different

types of sensors, with other kinds of information

– How do we support research?

– How do we make the information meaningful for

families, clinicians, researchers, and sensor

developers?

Results from Project 3

Results from Project 3 • Use case archetypes

Use cases identify information flow…

and data requirements Use Case C. (This use case is derived from a recently published manuscript by

Rosalyn Singleton of the Alaska Native Tribal Health Consortium and colleagues,

“Housing characteristics and indoor air quality in households of Alaska native

children with chronic lung conditions”).

This study focused on association between childrens’ respiratory

symptoms and measurements of indoor air quality. In order to measure

indoor air quality, the investigators deployed the following sensors: TSI

Inc. DustTrac Models 8530/8533 (PM2.5), Ultra III Passive Badge(VOCs),

Radiello 130 Passive Badge (VOCs), TelAir 7001 Sensor with a HOBO

U12 data logger (CO2, temperature, and relative humidity), ExTech

CO210 Indoor Air Quality CO2 Monitor (CO2, temperature, and relative

humidity), and Lascar Electronics Lascar300 (CO). …The sensors

generated data at varied time intervals …

Synergy

• Collaboration across projects

• Collaboration across PRISMS centers

– Data Modeling Working Group

– Discussions with Environmental Defense

Fund and other groups (examining

standards)

• Collaboration across similar work on

campus (DEQ/PRISMS)

New Collaborations Emerging

• Lots of interest in broader

community

• Lots of interest in

academic community

• Opening doors to new

collaborations

Our vision

A center where researchers and

practitioners integrate genetic,

environmental, behavioral and

clinical information to advance

precision medicine and health care

transformation for better

management of pediatric asthma.

Our Dream

Imagine this scenario…

The hospital command center predicts, with 87%

accuracy, that according to environmental

conditions tomorrow your clinic will have 47 patients

with exacerbated asthma conditions. According to

their genetic profile 59% should be treated with

protocol A, 37% with protocol B, and you should

make arrangements to admit the remaining 4 for

which serious complication can be expected.

PRISMS informatics center is the first step

Acknowledgements

This effort was supported by U54EB021973, National Institute of

Biomedical Imaging and Bioengineering, NIH and University of Utah Air

Quality Seed Grant Program. OpenFurther supported by NCRR/ NCATS

Grants UL1RR025764 and 3UL1RR025764-02S2, National Center for

Clinical and Translational Science 1UL1TR001067, University of Utah

Research Foundation, grant 1D1BRH20425 (DHHS), and R01

HS019862 from AHRQ, (DHHS).