2015/12/41 Infectious Disease Informatics: Overview and The BioPortal Experience Hsinchun Chen, Ph.D. Artificial Intelligence Lab, U. of Arizona NSF BioPortal.

112/04/18 1

Infectious Disease Informatics: Overview and The BioPortal Experience

Hsinchun Chen, Ph.D.Artificial Intelligence Lab, U. of ArizonaNSF BioPortal Research Center

Acknowledgement: NSF, CIA, DHS, CDC, NCI

Hsinchun Chen et al., 2005 Hsinchun Chen, et al., 2010

Medical Informatics: The computational, algorithmic, database and information-centric approach to the study of medical and health care problems.Infectious Disease Informatics: Medical informatics for infectious disease, public health, and biodefence.

112/04/18 3

IDI and Syndromic Surveillance Systems Data sources and collection strategies

Formal-informal (sequence to epi), standards, data entry and transmission, security

Data analysis and outbreak detection Syndromic classification, outbreak detection

methods (temporal, spatial, spatial-temporal), multiple data streams

Data visualization, information dissemination, and alerting GIS, temporal, sequence, text, interactive

System assessment and evaluation Algorithms, data collection, information

dissemination, interface, usability

112/04/18 4

Syndromic Surveillance Data Sources in Different Stages of Developing a Disease Reaching Situational Awareness

Reproduced from Mandl et. al. (2004)

112/04/18 5

Syndromic Surveillance Systems Generation 1, paper-based: paper, fax, TEL,

TEL directory, etc. Generation 2, email-based: email,

Word/Access, pager, cell phone, etc. Generation 3, database-driven: database,

standards, messaging, tabulation, GIS, graphs, text, etc.

Generation 4, search engine-based: real-time, interactive, web services, visualized, GIS, graphs, texts, sequences, contact networks, etc.

112/04/18 6

Syndromic Surveillance System Survey

Projects User population Stakeholders

RODS -Pennsylvania, Utah, Ohio, New Jersey, Michigan etc

-418 facilities connected to RODS RODS laboratory,

U of Pittsburgh

STEM N/A IBM

ESSENCE II 300 world wide DOD medical facilities DoD

EARS -Various city, county, and state public health officials in the United States and abroad of US

CDC

BioSense Various city, county, and state public health officials in the United States and abroad of US

CDC

RSVP Rapid Syndrome Validation Project; Kansas, NM Sandia NL, NM

BioPortal NY, CA, Kansas, AZ, Taiwan U of Arizona

112/04/18 7

Sample Systems and Data Sources Utilized

Projects Data sources/Techniques

RODS - Chief complaints (CC); OTC medication sales

- Free-text Bayesian disease classification

STEM - Simulated disease data

- Disease modeling and visualization, SIR

ESSENCE II - Military ambulatory visits; CC; Absenteeism data

EARS - 911 calls; CC; Absenteeism; OTC drug sales- Human-developed CC classification rules

BioSense - City/state generated geocoded clinical data- Graphing/mapping displays

RSVP - Clinical and demographic data - PDA entry and access

BioPortal - Geo-coded clinical data; Gemonic sequences; Multilingual CC- Real-time access and visualization; Web based hotspot analysis; Sequence visualization; Multilingual ontology-based CC classification

8

COPLINK System

•Newsweek Magazine March3, 2003

•ABC News April 15, 2003

•The New York Times November 2, 2002

COPLINK News

Dark Web System

Project Seeks to Track Terror Web Posts, 11/11/2007

Researchers say tool could trace online posts to terrorists, 11/11/2007

Mathematicians Work to Help Track Terrorist Activity, 9/14/2007

Team from the University of Arizona identifies and tracks terrorists on the Web, 9/10/2007

Dark Web News

112/04/18 12

BioPortal: Overview, West Nile Virus (real-time information collection, sharing, access,

visualization, and analysis, Epi data across species)

112/04/18 13

BioPortal Project Goals

Demonstrate and assess the technical feasibility and scalability of an infectious disease information sharing (across species and jurisdictions), alerting, and analysis framework.

Develop and assess advanced data mining and visualization techniques for infectious disease data analysis and predictive modeling.

Identify important technical and policy-related challenges in developing a national infectious disease information infrastructure.

112/04/18 14

Data Ingest ControlModule

Cleansing / Normalization

New

Info

-Sh

arin

g I

nfr

ast

ruct

ure

NYSDOH CADHS

XML/HL7Network

PHINMSNetwork

Adaptor Adaptor Adaptor

Portal Data Store(MS SQL 2000)

SS

L/R

SA

SS

L/R

SA

Information Sharing Infrastructure Design

112/04/18 15

Data Access Infrastructure Design

Web Server (Tomcat 4.21 / Struts 1.2)

Data Store(MS SQL 2000)

Dat

a S

tore

WN

V-B

OT

Po

rtal

Data Search and Query

Spatial-TemporalVisual-ization

HAN orPersonalAlertManagement

Analysis /Prediction

User Access Control API (Java)

DatasetPrivilegesManagement

Browser (IE/Mozilla/…)

Public healthprofessionals,

researchers, policy makers, law enforcement

agencies & other users

AccessPrivilegeDef.

SSL connection

112/04/18 16

Spatial-Temporal Visualization Integrates four visualization techniques

GIS View Periodic Pattern View Timeline View Central Time Slider

Visualizes the events in multiple dimensions to identify hidden patterns Spatial Temporal Hotspot analysis Phylogenetic tree Contact network analysis

112/04/18 17

BioPortal Prototype Systems

112/04/18 18

Outbreak Detection & Hotspot Analysis Hotspot is a condition indicating

some form of clustering in a spatial and temporal distribution (Rogerson & Sun 2001; Theophilides et. al. 2003; Patil & Tailie 2004; Zeng et. al. 2004; Chang et. al. 2005)

For WNV, localized clusters of dead birds typically identify high-risk disease areas (Gotham et. al. 2001); automatic detection of dead bird clusters can help predict disease outbreaks and allocate prevention/control resources effectively

112/04/18 19

Retrospective Hotspot Analysis Problem Statement

112/04/18 20

Risk-Adjusted Support Vector Clustering (RSVC)

Estimate baseline density

Minimum sphere

Feature space

High baseline density makes two points far apart in feature space

Split into several clusters

112/04/18 21

Study II: NY WNV (birds, mosquitoes, and humans)

On May 26, 2002, the first dead bird with WNV was found in NY Based on NY’s test dataset

March 5 May 26 July 2

baseline new cases

140 records 224 records

112/04/18 22

Dead Bird Hotspots Identified

112/04/18 23

User Login

Choose WNV disease data

User main pageAvailable dataset list

Select CA dead bird, chicken and NY dead bird data

Select CA dead bird, chicken and NY dead bird data

Advanced Search criteria

Positive cases

Positive cases

Positive cases

Specify bird species

Dataset name

Spatial / Temporal

Time range

County / State

Results listed in tableSelect background maps

Select NY / CA population, river and

lakes

Start STV

112/04/18 24

Zoom in NY

Timeline

PeriodicPattern

GIS

Control panel View all 3 year

data

Overall pattern

NY dead bird temporal distribution pattern

1 year window in 3 year span

Concentrated in May / Jun


Move time slider, year 2

Similar time pattern


Move time slider, year 3

Similar time pattern


Zoom in

CloseClose

Year 2001 data

Spatial distribution pattern


2 weeks window


112/04/18 25

Move time slider














Season end

Dead bird casesmigrate from long island

Into upstate NY

Enable population

map

Overlay population map

Dead bird casesdistribute along

populated areas nearHudson river

112/04/18 26

BioPortal HotSpot Analysis: RSVC, SaTScan, and CrimeStat Integrated (first visual, real-

time hotspot analysis system for disease surveillance)

West Nile virus in California

112/04/18 27

Regular STV

Hotspot Analysis-Enabled STV

Select baseline and case periods

Select algorithms

Select baseline and case periods

Select target geographic area

Hotspots found!

Select hotspot to

highlight case points

112/04/18 28

BioPortal – FMD(many species; phylogenetic tree and

news)

112/04/18 29

FMD Global Surveillance: Lessons Learned

Must understand risks, and nature of changing risks, in order to develop strategies for prevention and mitigation on a global scale

Must understand the global situation in order to prepare locally

United Kingdom FMD outbreak, 2001; $12B, 50-60% of 4M farm animals (cows, pigs, sheep) slaughtered

112/04/18 30

International FMD BioPortal

Real time web-based situational awareness of FMD outbreaks worldwide through the establishment of an international information technology system.

FMDv characterization at the genomic level integrated with associated epidemiological information and modeling tools to forecast national, regional and/or international spread and the prospect of importation into the US and the rest of North America.

Web-based crisis management of resources—facilities, personnel, diagnostics, and therapeutics.

112/04/18 31

Preliminary Global FMD Dataset Provider: UC Davis FMD Lab Information sources: reference labs and OIE Coverage: 28 countries globally Dataset size: 30,000+ records of which 6789 records are

complete Host species: Cattle, Caprine, Ovine, Bovine, Swine, NK,

Elephant, Buffalo, Sheep, Camelidae, Goat

Ovine37%

Bovine37%

Caprine4%

Cattle5%

Sheep3% Camelidae

0%

Goats0%

Swine11%

Elephant0%

Buffaloes3%

Regionwise Distribution of FMD Data

South America66%

Central and SouthAsia15%

Africa1%

Middle East Asia4%

Europe14%

112/04/18 32

Global FMD Coverage in BioPortal

112/04/18 33

FMD Migration Visualization using BioPortal (cases in South Asia)

FMD Cases travel back and forth

between countries

112/04/18 34

BioPortal-Afghanistan

112/04/18 35

International FMD News Provider: UC Davis FMD Lab Information sources: Google, Yahoo, and

open Internet sources Time span: Oct 4, 2004 – present (real-

time messaging under development) Data size: 460 events (6/21/05) Coverage: 51 countries

(Africa:11, Asia:16, Europe:12, Americas:12)

Africa11% Aisa

1%

Asia15%

Australia14%

Europe27%

America27%

UNDEFINED5%

112/04/18 36

Searching FMD News http://fmd.ucdavis.edu/ Searchable by

Date range Country Keyword

112/04/18 37

Visualizing FMD News on BioPortal

112/04/18 38

FMD Genetic Visualization Goal: Extend STV to incorporate 3rd

dimension, phylogenetic distance Include a phylogenetic tree. Identify phylogenetic groups and color-code the

isolate points on the map. Leverage available NCBI tools such as BLAST.

Proof of concept: SAT 2 & 3 analysis Data: 54 partial DNA sequence records in South

Africa received from UC Davis FMD Lab (Bastos,A.D. et al. 2000, 2003)

Date range: 1978-1998 Countries covered: South Africa,

Zimbabwe, Zambia, Namibia, Botswana

112/04/18 39

Textual View of Gene Sequence

Color-coded View (MEGA3)

Sample FMD Sequence Records

112/04/18 40

Phylogenetic Treeof Sample FMD Data

Identify 6 groupswithin 2 major families (MEGA3; based on sequence similarity)

Group4

Group2

Group3

Group1

Group5

Group6

112/04/18 41

Genetic, Spatial, and Temporal Visualization of FMD Data

Isolates’ locations color

coded

Phylogenetic tree color coded

Isolates’ appearances in

time

112/04/18 42

FMD Time Sequence Analysis

2nd family cases exist before 1993 and a comeback lately

First family cases appeared throughout

the period

Second family cases existed before 1993 and reappeared later

after 1997

112/04/18 43

FMD Periodic Pattern Analysis2nd family concentrated in

Feb. while 1st family spread evenly

112/04/18 44

Locations of Family 1 records

Selected only groups 1, 2, and 3 and found a spatial

cluster

112/04/18 45

Locations of Family 2 records

Selected only groups 4, 5, and 6

Sparse isolate locations

112/04/18 46

BioPortal: Influenza, SARS (chief complaint syndromic surveillance,

contact network analysis and visualization)

112/04/18 47

Existing CC Classification Methods

Classification Method Systems Authors

Keyword Match + Synonym List + Mapping Rules

DOHMH (NY City),

EARS

Mikosz et. al. (2004)

Weighted Keyword Match (Vector Cosine Method) + Mapping Rules

ESSENCE Sniegoski (2004)

Naïve Bayesian RODS Olszewski (2003), Ivanov et. al (2002)

Bayesian Network N/A Chapman et. al. (2004)

112/04/18 48

Syndromic Categories in Different Systems

System # Sdms

Details

CDC (2002)

11 Botulism, Hemorrhagic, Lymphadenitis, Cutaneous Lesion, Gastrointestinal, Respiratory, Neurological, Rash, Specific Infection, Fever, Severe Illness or Death

EARS 41 Lower Resp., Upper Resp., Neuro, Febrile, Poison, Hemorrhage, Botulinic, Rash, Fever, etc. (41 categories)

RODS 8 Gastrointestinal, Constitutional, Respiratory, Rash, Hemorrhagic, Botulinic, Neurological, Other

ESSENCE 8 Death, Gastr, Neuro, Rash, Respi, Sepsi, Unspe, Other

112/04/18 49

Overall System Design

Chief Complaints

CCStandardization

Symptom Grouping Syndrome

Classification

symptomsSymptomGroups Syndromes

Stage 1 Stage 2 Stage 3

EMT-P JESS

UMLSConcepts

SynonymList

EARSSyndrome

Rules

SymptomGrouping

Table

UMLS Ontology

EARSSymptom

Table

EMT-P

Weighted Semantic

Similarity Score

Weighted Semantic

Similarity Score

112/04/18 50

Comparing BioPortal to RODS

Trained BioPortal

Syndrome TP+FN PPV Sensitivity Specificity F F2

GI 124 91.41% 94.35%*** 98.74% 0.93*** 0.93***

HEMO 30 82.86% 96.67%*** 99.38% 0.89** 0.92***

RASH 15 66.67% 66.67%** 99.49% 0.67* 0.67**

RESP 110 92.08% 84.55%**** 99.10% 0.88*** 0.87***

UPPER_RESP 43 80.43% 86.05% 99.06% 0.83 0.84

RODS


GI 124 89.89% 64.52% 98.97% 0.75 0.71

HEMO 30 90.91% 66.67% 99.79%* 0.77 0.73

RASH 15 58.33% 46.67% 99.49% 0.52 0.50

RESP 110 87.84% 59.09% 98.99% 0.71 0.66

UPPER_RESP 43 N/A N/A N/A N/A N/A

* p-value < 0.1 ** p-value < 0.05 *** p-value < 0.01Statistical test is based on 2,500 bootstrapings.

112/04/18 51

Comparing BioPortal to EARS

Trained BioPortal


GI 124 91.41% 94.35%*** 98.74% 0.93*** 0.93***

HEMO 30 82.86% 96.67%*** 99.38% 0.89*** 0.92***

RASH 15 66.67% 66.67%** 99.49% 0.67 0.67*

RESP 110 92.08% 84.55%**** 99.10% 0.88*** 0.87***

UPPER_RESP 43 80.4%*** 86.05%*** 99.06%*** 0.83*** 0.84***

EARS


GI 124 93.75%* 72.58% 99.32%*** 0.82 0.78

HEMO 30 100.00%*** 33.33% 100.00%*** 0.50 0.43

RASH 15 70.00% 46.67% 99.70% 0.56 0.53

RESP 110 90.36% 68.18% 99.10% 0.78 0.74

UPPER_RESP 43 58.70% 62.79% 98.01% 0.61 0.61

* p-value < 0.1 ** p-value < 0.05 *** p-value < 0.01Statistical test is based on 2,500 bootstrapings.

112/04/18 52

Chinese CC Preprocessing: System Design

Separate Chinese and

English Expressions

Chinese Phrase

Segmentation

ChinesePhrase

Translation

ChineseExpressions

SegmentedChinese Phrases

TranslatedChinesePhrases

Stage 0.1 Stage 0.2 Stage 0.3

Chinese to English

Dictionary

ChineseMedical Phrases

CommonChinesePhrases

Raw ChineseCCs

MutualInfo.

Chinese Chief Complaints

English Expressions

112/04/18 53

Group by Hospital

112/04/18 54

Group by Syndrome Classification

112/04/18 55

Taiwan SARS Contact Network Visualization

Social network visualization with patients and geographical locations

Scroll bar on time dimension to see the evolution of a network

112/04/18 56

Taiwan SARS Network Evolution – Hospital Outbreak

The index patient of Heping Hospital began to have symptoms.

112/04/18 57

BioPortal:Towards building integrated, real-time situation

awareness for syndromic surveillance and biodefense

112/04/18 58

Syndromic Surveillance vs. BioSurveillance (Species Jumps) Data sources and collection strategies

Levels of data (sequence to epi to social media), data granularity, automated/manual, integration, information sharing (incentives and standards)

Data analysis and outbreak detection Biological/genetic modeling/analytics

(hypothesis testing), exploration, hypothesis generation, sequence to time/space/event

Data visualization, information dissemination, and alerting Target users (DTRA), dissemination strategies,

surveillance, prediction System assessment and evaluation

Target users (DTRA), acceptance, usability, situation awareness, decision making

112/04/18 59

BioPortal Information

Hsinchun Chen [email protected]

AI Lab project information http://ai.arizona.edu

2015/12/41 Infectious Disease Informatics: Overview and The BioPortal Experience Hsinchun Chen, Ph.D. Artificial Intelligence Lab, U. of Arizona NSF BioPortal.

Documents

data collection

data entry

usability slide

nci slide

security data analysis

coplink system slide

coplink news slide

dark web system slide