luciano informs healthcare_2015 nashville, tn usa july 30 2015

Health Web Observatories: Creating Preferable Health Outcomes

through

Health Web Science Joanne S. Luciano, PhD

Predictive Medicine, Inc., Belmont, MA (predmed.com)

Rensselaer Polytechnic Institute, Troy, NY

30 July 2015 INFORMS Healthcare Conference 2015

Nashville, Tennessee, USA

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PRESENTER

Joanne S. Luciano

Enable Health and Wellbeing through Knowledge Technology

BS, MS Computer Science PhD Cognitive and Neural Systems (Computational Neuroscience) Wang Labs Harvard Medical School MITRE Lotus Development Predictive Medicine, Inc. Rensselaer Polytechnic Institute GE Global Research Labs

Interests Flying planes, rocks: climbing, balancing and photographing them

Community BioPathways Consortium, BioPAX, W3C HCLSIG, Yosemite Project, FIBO

Email:

[email protected]

[email protected] Always open to exploring opportunities.

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Multidisciplinary International Team

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Grant Cumming, Medical Doctor, NHS Grampian, Honorary Professor, University of the Highlands and Islands, AB24 2ZN, Aberdeen, United Kingdom, [email protected]

Tara French,Research Fellow, Institute of Design Innovation, The Glasgow School of Art, Horizon Scotland, Digital Health Institute, Forres IV36 2AB, United Kingdom, [email protected] Eva Kahana,Distinguished University Professor and The Pierce T. and Elizabeth D. Robson Professor of the Humanities, Case Western Reserve University, Mather Memorial Building 231B, Cleveland OH 44106, United States of America, [email protected] David Molik,Computational Developer, Cold Spring Harbor Laboratories, One Bungtown Road, Cold Spring Harbor NY 11724, United States of America, [email protected]

Objectives �  Formulating Healthcare for the 21st Century

�  Are we where we should be?

�  What’s missing? �  How do we use the Web?

�  How can we use the Web? �  How do we know what will work? �  What are the tools, technologies, and resources

needed? �  How do we evaluate effectiveness?

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Brendan Ashby Master’s Thesis (RPI)

Actively SEEKING FUNDING

Nightingale

Research to Practice Timeline (earlier work: 10 years in Software Research & Development and Product Development)

2009 1993

World Congress on Neural Networks, July 11-15, 1993, Portland, Oregon SIG Mental Function and Dysfunction Sam Levin

Jackie Samson, Mc Lean Hospital Depression Research

1996

1995

2008 1994

Patents Sold to Advanced

Biological Laboratories

Belgium

Patents Offered at Ocean Tomo

Auction Chicago, IL

US Patent No. 6,317,73 Awarded

US Patents No. 6,063,028

Awarded

2001

2000

PhD

Thesis Proposal Approved

Workshop Neural Modeling of Cognitive and Brain Disorders

BioPAX

? Linked Data W3C HCLS BioDASH

EPOS

2006

EMPWR

Poster Presented ISMB 1997 PSB 1998

1997

2010

Rensselaer (RPI)

2011 2012 2013

U Pitt Greg Siegle Depression

Collaboration

Yuezhang Xiao

Master’s Thesis (RPI)

Failed to get Funding for Proactive

Multimodal Depression Treatment

Health Web Science

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2014 2015

Is 15-20 years too long to get from research to practice?

Healthcare Singularity and the age of Semantic Medicine

http://research.microsoft.com/en-us/collaboration/fourthparadigm/4th_paradigm_book_part2_gillam.pdf

2,300 years after the first report of angina for the condition to be commonly taught in medical curricula, modern discoveries are being disseminated at an increasingly rapid pace.

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Healthcare Singularity and the age of Semantic Medicine

http://research.microsoft.com/en-us/collaboration/fourthparadigm/4th_paradigm_book_part2_gillam.pdf

Focusing on the last 150 years, the trend still appears to be linear, approaching the axis around 2025.

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Times have changed �  Aging population (end of life costly) �  More people with chronic illnesses

(increased cost) �  The end of the blockbuster era (decrease

in revenues, increase in drug development cost)

�  Need lower drug development cost �  Personalized Medicine (right treatment to

the right patient at the right time) �  Improved patient response to treatment

(Evidence Based) �  Web and Mobile

�  The technology (ubiquitous, monitor) �  Patient engagement increasing

8 Photos: http://www.flickr.com/photos/sepblog/4014143391/ http://allthingsd.com/files/2013/07/photo-12.jpg

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Data Driven Medicine: 3 Shifts in thinking and practice: � Data, Not Programs (reuse!) � Sharing, Not Hoarding (or hiding) � Personal, Not (only) Population

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Data Sharing

10 http://www.youtube.com/watch?v=N2zK3sAtr-4 7/30/15

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Health Web Observatories: Creating Preferable Health Outcomes

through Health Web Science

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The impact of the personal computer and internet on an individuals potential to influence society.

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Health Web Science recognizes the revolutionary impact of the Internet, made possible through the Web, with the potential to change health behaviors and health care worldwide. This impact on changing the practice of medicine can be considered in three areas: power, experience and speed.

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Web Science (WS) Web Science is about investigating how human behavior co-constitutes the Web. People who impose regulations, engineer the Web, produce content, or even just click on links change the Web how other people will see it. Vice versa, what people see and do on the Web will change their behavior. Web Science is about understanding this cycle.

SteffenStaab

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1/3 world’s population use the Web [1] 80% look for health information online [2] •  Studies impact of the Web on health and wellbeing •  Aims towards a preventative, participatory, personalized,

and predictive (P4) model of healthcare. •  Posits P4 can be achieved by the leveraging of the Web’s

data, resources and nature. •  Studies the evolving social, political, economic, policy

health related questions that emerge as a result of the use of the Web.

Health Web Science (HWS)

[1] Miniwatts Marketing Group 2012 [2] California Healthcare Foundation, Fox, S. 2011

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The World Wide Web •  Directly influences conscious behavior (Kahneman, System 2) through imparting information •  Indirectly influences unconscious behavior (Kahneman, System 1) through social interactions

•  “co-conscious” interactions are the emergent collective consciousness of the networ

The Web and Human Behavior Influence Health Outcomes

HWS seeks to understand the dynamics of these behavioral influences in order to support users in achieving better health outcomes

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Instruments for Web Study – what works and what doesn’t, i.e. when to use technology, policy, transparency?

•  Enable data to be found •  Make the metadata available for use by others •  Study the data in context using metadata •  Aggregation and presentation of observations enable

a feedback mechanism for preferable futures.

A health Web Observatory is a system that gathers and links to health data on the Web in order to answer questions about the Web, the users of the Web and the way that they affect each other within the context of healthcare.

Health Web Observatory (HWO)

How? Technologies Needed to enable Health Web Science and the vision for 21st Century Medicine

It’s all about the meaning!

� Semantic Enabling: Web Observatories

� Semantic Interoperability: � Shared Meaning: Yosemite Project �  Inference: Ontologies and OWL � Linked Data: RDF, HTTP, URIs as

terms

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Enabling Web Observatories

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terms

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Unified Medical Language System Knowledge Sources

The UMLS has three tools, called the UMLS Knowledge Sources:

�  Metathesaurus: Terms and codes from many vocabularies, including CPT®, ICD-10-CM, LOINC®, MeSH®, RxNorm, and SNOMED CT®

�  Semantic Network: Broad categories (semantic types) and their relationships (semantic relations)

�  SPECIALIST Lexicon and Lexical Tools: Natural language processing tools

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terms

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Ontology Spectrum

http://www.mkbergman.com/wp-content/themes/ai3v2/images/2007Posts/070501d_SemanticSpectrum.png

Strong Semantics

Weak Semantics

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Ontology Spectrum

Reuse of terminological resources for efficient ontological engineering in Life Sciences by Jimeno-Yepes, Antonio; Jiménez-Ruiz, Ernesto; Berlanga-Llavori, Rafael; Rebholz-Schuhmann, Dietrich Journal: BMC Bioinformatics Vol. 10 Issue Suppl 10 DOI: 10.1186/1471-2105-10-S10-S4

http://www.mkbergman.com/wp-content/themes/ai3v2/images/2007Posts/070501d_SemanticSpectrum.png

Existing formalisms

Strong Semantics

Weak Semantics

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Application vs. Reference Ontology

Reference Ontology �  Intended as an authoritative source �  True to the limits of what is known (this changes!) �  Used by others

�  Application Ontology �  Built to support a particular application (use case) �  Reused rather than define terms �  Skeleton structure to support application �  Terms defined refine or create new concepts directly or

through new classes based on inference

http://www.nlm.nih.gov/research/umls/presentations/2004-medinfo_tut.pdf

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Healthcare and Life Science Goal: a suite of orthogonal interoperable reference ontologies

Barry Smith U Buffalo, NCBO From: Nat Biotechnol. 2007 November; 25(11): 1251. doi: 10.1038/nbt1346

The Open Biological and Biomedical Ontologies http://www.obofoundry.org

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How?

Technologies Needed to enable Health Web Science and the vision for 21st Century Medicine




terms

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The Open Biological and Biomedical Ontologies

From: Nat Biotechnol. 2007 November; 25(11): 1251. doi: 10.1038/nbt1346

http://www.obofoundry.org

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Translational Medicine Ontology

Overview of selected types, subtypes (overlap) and existential restrictions (arrows) in the Translational Medicine Ontology.

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34 The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside Luciano et al. Journal of Biomedical Semantics 2011, 2(Suppl 2):S1 http://www.jbiomedsem.com/content/2/S2/S1

Bridge the Gap Between “Bench and Bedside”

Translational Medicine Knowledge Base Translational

Medicine Ontology with mappings to ontologies and terminologies listed in the NCBO BioPortal. The TMO provides a global schema for Indivo-based electronic health records (EHRs) and can be used with formalized criteria for Alzheimer’s Disease. The TMO maps types from Linking Open Data sources.

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Individuals, Not Populations

36 Photo: http://www.flickr.com/photos/sepblog/4014143391/

http://safety-code.org/

Quickly retrieve pharmacogenomic markers of patients when needed No central storage of data is necessary, giving patients full control over their personal health information.

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Application Ontology Influenza Ontology

http://www-test.ebi.ac.uk/industry/Documents/workshop-materials/DiseaseOntologiesAndInformation190608/The%20Influenza%20Infectious%20Disease%20Ontology%20(I-IDO)%20-%20Joanne%20Luciano.pdf

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Conclusion Creating Preferable Health Outcomes through Health Web Science

�  Web Science

�  Health Web Observatories as web tools �  Semantic Technologies �  Standards and Interoperability

Web Observatories are VERY EARLY STAGE in HEALTH

�  Health Web Sciences Needs your help!

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https://www.baby-connect.com/images/baby2.gif

https://encrypted-tbn3.gstatic.com/images?q=tbn:ANd9GcTFXOU0CsGM8pddeiadAbtTirgIv-_3KeaL_fhKIYYFAMPEOTy3

Thank You!

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What is UMLS? The UMLS, or Unified Medical Language System

Enables Interoperability between computer systems �  Files �  Software

that brings together many health and biomedical �  vocabularies and standards

You can use the UMLS to enhance or develop applications, such as electronic health records, classification tools, dictionaries and language translators.

http://www.nlm.nih.gov/research/umls/presentations/2004-medinfo_tut.pdf http://www.nlm.nih.gov/research/umls/quickstart.html

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Unified Medical Language System

Access to the UMLS The UMLS Terminology Services (UTS) provides three ways to access the UMLS:

�  Web Browsers You can search the data through UTS applications: �  Metathesaurus Browser - Retrieve UMLS concept information,

including CUIs, semantic types, and synonymous terms. �  Semantic Network Browser - View the names, definitions, and

hierarchical structure of the Semantic Network.

�  Local Installation download, customize and load into your database system, or browse your data using the MetamorphoSys RRF browser.

�  Web Services APIs You can use NLM’s application programming interfaces (APIs) to query the UMLS data within your own application.

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License Required �  Request a license (FREE)

�  Sign up for a UMLS Terminology Services (UTS) account.

�  UMLS licenses are issued only to individuals

�  NLM is a member of theIHTSDO (owner of SNOMED CT), and there is no charge for SNOMED CT use in the United States and other member countries. Some uses of the UMLS may require additional agreements with individual terminology vendors.

The UTS account allows you to browse, download, and query the UMLS.

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Use UMLS to link health information, medical terms, drug names, and billing codes across different computer systems.

Some examples: �  Linking terms and codes between doctor, pharmacy, and

insurance company �  Patient care coordination among several departments within a

hospital �  SNOMED, ICD-9, LOINC, RxNorm – clinical setting, more

about this later in the next part of the tutorial

The UMLS has many other uses, including search engine retrieval, data mining, public health statistics reporting, and terminology research.


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Overview

Introduction (10 minutes) 1.  Background

1.  BioMed Domain – Health care and Life Science 2.  Reference and Application 3.  Ontology Granularity and Layout

2.  Examples: (40 minutes) 1.  Reference Ontology Examples

1.  UMLS – High level across biomedicine (5)

2.  BioPAX – Mid level – biological pathways (10) 3.  Gene Ontology (“GO”) – Gene annotation (5)

2.  Application Ontology Examples 1.  Influenza Ontology (5) 2.  Best Practices (10)

3.  Conclusion (5 minutes) 1.  Process: Start with Use Case, develop prototype, Evaluation 2.  Standards: BioMedical Ontology Best practices (BioPortal, BFO, SIO) 3.  Conferences

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Examples

3 Reference Ontology Examples � UMLS – High level across biomedicine � BioPAX – Mid level – biological pathways � Gene Ontology (“GO”) – Gene annotation

2 Application Ontology Example � Influenza Ontology � Translational Medicine Ontology

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BioPAX Biological PAthway

eXchange

An abstract data model for biological pathway integration

Initiative arose from the community

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Metabolic Pathways BioPAX Level 1

Biological Pathways of the Cell BioPAX

A series of chemical reactions, catalyzed by enzymes The products of one are the reactants of the next e.g. Conversion, Transport 7/30/15

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BioPAX Level 2

BioPAX Biological Pathways of the Cell

Cells are complex systems whose physiology is governed by an intricate network of Molecular Interactions (MIs) of which a relevant subset are protein–protein interactions (PPIs).

Molecular Interaction Networks

http://www.estradalab.org/research/

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http://www.estradalab.org/research/

Human Protein Interaction Network (PIN)

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BioPAX Level 2

Biological Pathways of the Cell

Adapted from Cell Signalling Biology - Michael J. Berridge - www.cellsignallingbiology.org - 2012 and http://www.hartnell.edu/tutorials/biology/signaltransduction.html

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Signaling Pathways

BioPAX Level 3

BioPAX

Signaling molecules trigger cellular responses. Molecules bind to the cell surface causing a cascade of activation Reactions

A activates B activates C….

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Gene Regulation


The modulation of any of the stages of gene expression that control: which genes are switched on and off when, how long, and how much Gene regulation may occur many stages: Transcription Post-transcriptional modification RNA transport Translation mRNA degradation Post-translational modifications among many others (more recently discovered!)

http://www.biology-online.org/dictionary/Gene_regulation

http://en.wikipedia.org/wiki/Regulation_of_gene_expression

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Metabolic Pathways


Signaling Pathways

Gene Regulation

BioPAX Level 1

BioPAX Level 2

BioPAX Level 3

BioPAX Level 4

BioPAX What’s a pathway? Depends on who you ask!

Biological Pathways of the Cell

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BioPAX Ontology

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Level 1 v1.0 (July 7th, 2004)

parts

how the parts are known to interact

a set of interactions

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BioPAX Biochemical Reaction

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phosphoglucose isomerase 5.3.1.9

OWL (schema)

Instances (Individuals)

(data)

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Before BioPAX With BioPAX

Common “computable semantic” enables scientific discovery

>200 DBs and tools

Database

Application

User

BioPAX - Simplify

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Examples



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Gene Ontology (GO) Standard representations:

�  Gene and gene product attributes

�  Across species and databases

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Structured controlled vocabularies organized as 3 independent Ontologies

�  Molecular Interactions

�  Biological Processes

�  Cellular Location

Gene Ontology Two Key Uses:

�  Resource: to look up genes with similar functionality or location within the cell to help characterize the function of a sequence or structure

� Use to annotate genomes to enable the analysis of the genome through the annotation terms.

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Gene Ontology Evidence Codes

Adapted from: http://people.oregonstate.edu/~knausb/rna_seq/annot.pdf Rhee, S.Y, Wood, V., Dolinski, K. and Draghici, S. 2008. Use and misuse of the gene ontology annotations. Nature Reviews Genetics 9:509-515. See also: http://www.geneontology.org/GO.evidence.shtml

Manually-assigned evidence codes fall into Four categories:

Experimental Computational analysis Author statements, Curatorial statements

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Inferred from Electronic Annotation (IEA) is not assigned by a curator.

Sequence Ontology Sequence Ontology (SO) ‘terms and relationships used to describe the features and attributes of biological sequence.’ (E.g., binding_site, exon, etc.)

SO http://www.sequenceontology.org/

sequence_attribute feature_attribute polymer_attribute sequence_location variant_quality

sequence_feature junction region sequence_alteration

sequence_variant functional_variant structural_variant

Relationship (lots!)

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(snuck this one in as another example)

Overview




1.  UMLS – High level across biomedicine (5) 2.  BioPAX – Mid level – biological pathways (10) 3.  Gene Ontology (“GO”) – Gene annotation (5)



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Examples



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Application vs. Reference Ontology

Reference Ontology �  Intended as an authorative source �  True to the limits of what is known �  Used by others

�  Application Ontology �  Built to support a particular application (use case) �  Reused rather than define terms �  Skeleton structure to support application �  Terms defined refine or create new concepts directly or

through new classes based on inference


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http://www-test.ebi.ac.uk/industry/Documents/workshop-materials/DiseaseOntologiesAndInformation190608/The%20Influenza%20Infectious%20Disease%20Ontology%20(I-IDO)%20-%20Joanne%20Luciano.pdf 7/30/15

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Overview







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Examples



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Overview







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Best Practices Semantic Web Methodology & Technology Development Process

Fox, Peter & McGuinness, Deborah 2008 http://tw.rpi.edu/web/doc/TWC_SemanticWebMethodology 7/30/15

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Generalized Ontology Evaluation Framework (GOEF)

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Two stages: 1.  Recast use case into its components: Three Levels of Evaluation 2.  Evaluate components using objective metrics

BioPortal http://bioportal.bioontology.org/

Provides access to commonly used biomedical ontologies and to tools for working with them. BioPortal allows you to

�  Browse �  the library of ontologies �  mappings between terms in different ontologies �  a selection of projects that use BioPortal resources

�  Search �  biomedical resources for a term �  for a term across multiple ontologies

�  Receive recommendations �  on which ontologies are most relevant for a corpus

�  Annotate text �  with terms from ontologies

All information available through the BioPortal Web site is also available through the NCBO Web service REST API. Please see REST API documentation for more information.

http://www.bioontology.org/wiki/index.php/NCBO_REST_services

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Conferences

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Conference on Semantics in Health Care and Life Sciences (CSHALS)

Semantic web applications and tools for life sciences (SWAT4LS)

Edinburgh 2013

Conclusion Tutorial sources

�  BioPortal

�  W3C HCLSIG

Consortia to join �  W3C HCLSIG

�  OpenPHACTS

�  Identifiers.org

�  Pistoia Alliance

�  BioPAX (check for new name)

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THANK YOU! RPI Tetherless World Constellation RPI Web Science Research Center Predictive Medicine, Inc. W3C Health Care & Life Science SIG BioPathways Consortium BioPAX Harvard Medical School, Mass General Hospital

Abha Moitra, Petr Haug, Larry Hunter, Bob Powers, Scott Marshall, Matthias Samwald, Michel Dumontier, Ted Slater, Eric Neumann, Lynette Hirschman, Lynn Schriml, Rick Lathrop and many many others! NSF, NIH, NIST, IEEE and many others!

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Backup Slides

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HL-7 and RIM HL-7 and RIM: http://www.w3.org/2013/HCLS-tutorials/RIM/#%286%29

�  RDF RIM Tutorial Eric Prud'hommeaux, <[email protected]>

�  Basic understanding of the structure of how data written in HL7's RIM can be expressed in RDF.

�  It is not a substitute for HL7's documentation, but instead the author's notion of a quick way to familiarize oneself with the concepts and terms used in the RIM and how the graph structure of RDF is a natural way to represent this data.

Copyright © 2013 W3C ® (MIT, ERCIM, Keio, Beihang) Usage policies apply.

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Personalized Medicine Components

•  Understand disease heterogeneity

�  Comprehend disease progression

•  Determine genetic and environmental contributors

�  Create treatments against relevant targets �  drugs against relevant targets (molecular structures) �  Yoga against stress �  Exercise against obesity �  Elimination against food intolerance or allergy

•  Develop markers to predict response

•  Identify concrete endpoints to measure response

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Scope Ontology Uses

�  Knowledge Management �  Annotate data (such as genomes) �  Access information (search, find, and access) �  Map across ontologies relate

�  Data integration and exchange �  Model dynamic cellular processes �  Identify Drug Interactions

�  Decision support �  SafetyCodes �  Diabetic Care �  Lab Alerts

(Bodenreider YBMI 2008) http://themindwobbles.wordpress.com/2009/05/04/olivier-bodenreider-nlm-best-practices-pitfalls-and-positives-cbo-2009/ 7/30/15

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Unified Medical Language System Metathesaurus

NLM uses the Semantic Network and Lexical Tools to produce the Metathesaurus.

Metathesaurus production involves: �  Processing the terms and codes using the Lexical Tools �  Grouping synonymous terms into concepts �  Categorizing concepts by semantic types from the

Semantic Network �  Incorporating relationships and attributes provided by

vocabularies �  Releasing the data in a common format

They can be accessed separately or in any combination according to your needs.

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luciano informs healthcare_2015 nashville, tn usa july 30 2015

Health & Medicine

research fellow

health web science joanne

health web observatories

digital health institute

software research development

funding nightingale

rensselaer rpi

united kingdom