Elsevier Smart Content LDR SemTech NYC Oct-17-2012

Elsevier Health Sciences

SemTechBiz 2012 Conference October 17, 2012

Alan Yagoda VP, Business Technology [email protected] @alanyagoda

Smart Content Drives Smart Applications The Future Of Using Knowledge In Healthcare

Elsevier is the largest Science, Technical and Medical Publisher in the world. In the area of Health Sciences, Elsevier publishes leading brands including The Lancet, Braunwald’s Heart Disease, Gray’s Anatomy, and the Netter Atlases among others. In addition, Elsevier produces leading online clinical support tools and products including:

•  Clinical Key •  MD Consult •  Procedures Consult •  Mosby’s Nursing Consult •  CPMRC Nursing Care Plans •  Gold Standard Drug Database •  MEDai Analytics for Managed Care Plans

Copyright © 2012 Elsevier, Inc. | All Rights Reserved

About Elsevier

The Challenge


The Challenge: Getting doctors the right information to make the best decisions and provide the best clinical care

Trusted: Authoritative medical and surgical content from Elsevier. Comprehensive: Integrated Medline and 3rd party content. Speed To Answer: Fast discoverability of the most relevant answers and more intuitive searching.


Introducing Smart Content


Copyright 2011 Outsell Gilbane Services, Inc.

http://www.outsellinc.com http://gilbane.com/xml/2009/11/what-is-smart-content.html#ixzz0hnuRhaBc

Taxonomy-Powered Content = Smart Content

Content today with structured XML

Content with applied taxonomy


Smart Content At Elsevier

Entities, concepts and relationships

Smart Content Applications

Better understanding through analysis and visualization • Question & Answer"• Actionable Content & Alerts"• Tag clouds • Heatmaps"• Animations"

Better discovery through semantic search & navigation • Faceted search & browse • Ontology-driven navigation • Task-specific results • Personalized/localized results • Link to evidenced-based content"

New knowledge through aggregation and synthesis • Topic pages • Social network maps • Geolocation maps • Data integration and mashups"• Text mining "• Inference and Reasoning

Images

Text

Tables

Elsevier Content

Elsevier knowledge organization systems

Linked data from partners and the Web

Partner Content

7 Copyright © 2012 Elsevier, Inc. | All Rights Reserved

Co

ncep

t Mapping

Making Smart Content Work in the Clinical Setting

250K+ Core Clinical Concepts

1M+ Hierarchical RelaIonships

1M+ Ontological RelaIonships

1M+ Synonyms

•  Vast amounts of content made easily discoverable •  Specialty-‐specific naviga9on

•  Dynamic clinical summary crea9on •  Meaningful related content recommenda9ons

PaIent Ed Drug Info Procedural Videos

Clinical Summaries

EMMeT

Elsevier Custom

UMLS

Books

Journals Guidelines Clinical Trials

Elsevier Merged Medical Taxonomy (EMMeT)


Introducing EMMeT (Elsevier Merged Medical Taxonomy)

Medical Name Malignant Neoplasm of the Breast

Consumer Friendly Name Breast Cancer

Synonyms Malignant Tumor of Breast Malignant Breast Neoplasm Breast Ca

Codes ICD9 – 174.9 MeSH – D001943 SNOMED-CT – 190121004

Semantic Type/Group Neoplastic Process/Disease

•  Breast Disorders •  Cancer of the Thorax •  Mammary Neoplasms •  More….

•  Breast Sarcoma •  Familial Breast Cancer •  Malignant lymphoma of the Breast •  Malignant Neoplasm of the breast outer

quadrant •  More…

Symptoms

Diagnostic Procedures

Treatment Procedures

Medications

Risk Factors

Prevention

Complications

Breast Lump, Nipple Retraction, …..

Mammography, Breast Biopsy, …..

Chemotherapy, Mastectomy, ….

Tamoxifen, Doxorubicin, …..

Family History, Genetics, Predisposition, ….

Screening, Preemptive Mastectomy, ….

Metastatic Cancer, ….

Parent Terms

Sem

antic

Rel

atio

nshi

ps

Children Terms

4

2

3

1


Automated Indexing: Weighted Tags for Better Search

Paragraph-level SMART Content tags uncover highly-relevant information not necessarily evident from the title or abstract alone.

Article-level SMART Content tags help confirm relevance and provide a topical overview about a piece of content.


EMMeT Powered Auto-Suggest


Linked Data Repository


Rivastigmine, a cholinesterase inhibitor, has been used to treat delirium in elderly patients with stroke. 1 A biologically plausible premise—that impaired cholinergic transmission might either cause or worsen delirium—led to a randomised, placebo-controlled, double-blind trial by Maarten van Eijk and colleagues 2 in The Lancet in which they added rivastigmine or placebo to usual treatment of patients in intensive care. The trial was halted at 104 patients by the drug safety and monitoring board (DSMB) because of increased mortality (12/54 in the rivastigmine group, 4/50 in the placebo group; p=0·07) and a worse outcome. The rivastigmine group …

Linked Data Repository (LDR): Warehouse for Smart Content Enhancements Delirium treatment: An unmet challenge Title

Drug Clinical finding

•  Service platform that provides a rich semantic layer that enables search and discovery of metadata.

•  Transforms content into knowledge data to allow exploration of extracted knowledge, content analysis, and visualization.

•  Enhances extracted knowledge of Elsevier assets by interlinking data with related sources of medical and scientific content and data.

•  Optimized for high-volume read-write for use by end-user products.

•  Provide service layer APIs for ease of integration.

Disease


ATC: N06DA03 Drug: RivasIgmine

med:diseases Delirium med:drugs RivasIgmine

Elsevier

Trial: NCT00623103 IntervenIon: RivasIgmine CondiIon: Delirium

LinkedCT Trial: NCT00623103 Serious Adverse events: Atrial fibrillaIon

owl:same as

owl: same as

foaf:page

Represent Enhancements and Vocabularies In RDF Satellites


LDR

Example RDF Statements  Tags from a taxonomy for a given document  Document sections relevant to a given concept  Document sections providing answers to a given question  Genes mentioned in a given document  Documents supporting or disputing conclusions of a given document  Concepts in the areas of expertise for a given author

Creation of Satellite Standards •  Linked data compliant RDF representing metadata objects •  Leverage common namespaces from dct, pav, rdf, skos •  Taxonomies in SKOS to enhance portability in the linked data world •  Subject tagging against a vocabulary representing extracted

knowledge •  Concept URIs that can be equated to URIs in linked data •  Support RDF/XML and Turtle

Discovery Services (Semantic Knowledgebase)

Data Space Services

LDR Semantic Infrastructure

15

Linked Data Pipeline Services (Hadoop)

JSON Transform

N-Quads Extract

Reasoning

Interlinking

RDF Loader

Ontology Svcs

Analytics

…

AnnotaIon Satellites

Linked Data Loader

MongoDB NoSQL

Access & Entitlements

Asset Satellites

Vocab Satellites

3rd Party Data

SOLR/SIREn

Admin & Monitoring Analytics Atom Feed

Discovery Svc API (REST)

Ontology Service SPARQL Alerts

Virtuoso Triplestore


AWS Cloud Management

Tagging and Indexing Services (Concepts, Chapters, ArIcles, Guidelines,etc) RDF GeneraIon

EMMeT Semantic Network

Vocabulary SKOS GeneraIon

Elsevier

Conten

t

Product-specific Smart Content Search Index

3rd P

arty

Conten

t

InsIt.

Conten

t

Smart Content Indexing Pipeline

Linked Data

Amazon S3

Vocab & Annotation RDF Satellites

Linked Data & 3rd Party Data

Smart Content In Action


Speed to Answer: Most relevant preview

Trend Analysis Of Special Health Topics (Mashups)


Improved Editorial Workflow: Smart Collection Tool



Comprehensive Drug Reference

•  Moving world-class content online to Point of Care. •  Extracted knowledge is linked for further enrichment. •  Information is condensed, immediate and actionable.

Integrated Drug Discovery Research



•  Recommended research relevant to a paIent profile •  InfoBu^on integraIon •  Alerts on FDA Announcements.

Linking Patient Data To Evidence-Based Research

Key Learnings


What We Learned On Our Journey To Adoption


• Focus on business value -‐ POCS are invaluable. • Requires different skillsets. • Break from tradiIonal IT and follow consumer Internet businesses.

Organiza2onal Readiness

• Cloud to combine big data and semanIcs at scale. • Product and data integraIon just got easier. • Need access points besides SPARQL. • Won’t get it right the first Ime -‐ Fail fast and cheap.

Technical

• Pick standards that are pracIcal and ease adopIon. • Be paranoid about freshness and quality of linked data. • OpImize for performance – more triples isn’t always be^er.

Data

Thank you. Alan Yagoda [email protected] @alanyagoda


Health & Medicine

Elsevier Smart Content LDR SemTech NYC Oct-17-2012