Beyond Health 2.0: the semantic web and intelligent

systems

Erik van Mulligen PhD

Marc Weeber PhD

Ravi Kalaputapu PhD

Erasmus University Medical Center, Rotterdam, The Netherlands

Knewco Inc, New York, United States of America

Netherlands Institute for Health Sciences (NIHES)

Netherlanss Center for BioInformatics (NBIC)

Health Users

John has already been using celebrex for a few years to

kill the pain of his reumatoïde artritis.

Recently he got stomach complaints, in particular pain.

As a preparation for his visit to the general practitioner he

is browsing the internet to find related information.

Is he able to combine information from different sources

(health sites, scientific literature, pharmaceutical sites) to

such an extent that he is well informed?

What tools would be necessary to locate and link right

information?

Rationale

To provide health information consumers (lay people, patients, scientists)

with relevant, useful (additional) information when consulting information (on

the web).

Create a semantic web on top of existing

information sources that links information

topics from different sites and databases

and with different modalities (text, video).

Assist health information consumers with

finding relevant, reliable information from

the information avalanche.

• much useful and relevant legacy data

and web pages

• semantic web technology still under

development

• approaches to overlay semantics on

the current web

• combination strategies

overlay current web with semantics layer

web 1.0 & 2.0

semantic web

mapping

Semantic Mining

Observational data

celecoxib causes upper gastrointestinal hemorrhage

celebrex causes Upper Gastro-Intestinal Bleeding

Peer reviewed data

Ontology

Diseases C001

Upper Gastro-Intestinal Bleeding

Upper gastrointestinal

hemorrhage

Drugs Celecoxib Celebrex

EHR

BioBanks

Studies

Literature

Guidelines

Protocols

Triple store

RDF/OWL

Ontology

EHR

BioBanks

Studies

Literature

Guidelines

Protocols

Nonsteroidal anti-inflammatory drugs (NSAIDs) are commonly used, but

have risks associated with their use, including significant upper

gastrointestinal tract bleeding. Older persons, persons taking

anticoagulants, and persons with a history of upper gastrointestinal tract

bleeding associated with NSAIDs are at especially high risk.

nonsteroidal anti-

inflammatory drugs

upper gastrointestinal tract

bleeding

anti-coagulants

older persons

causes increase

risk

increase

risk

increase risk

nonsteroidal anti-inflammatory drugs causes upper gastrointestinal tract bleeding Triple store

RDF/OWL older persons

anti-coagulants increase risk

upper gastrointestinal tract bleeding

upper gastrointestinal tract bleeding

EHR

BioBanks

Studies

Literature

Guidelines

Protocols

ontology development

-NCBO

-Unified Medical Language System

-SNOMED CT

OWL/RDF triple formalisms

-nano publication

-aggregation methods: association, mutual information

specific projects

-EU-ADR: detecting new side effects for drugs from observational data

-OpenPHACTS: combining triples for drug discovery

-CALBC: harmonization & alignment of different NER systems in a large corpus

-Semantic MedLine: semantic relations between entities in PubMed

Example: EU-ADR

Data extraction: periodic

Signal detection

Signal substantiation

Retrospective and prospective

signal validation

Literature

Known side

effects

Pathway

analysis

In-silico

simulation

Medical databases: 30 Million persons (IT, NL, UK, DK)

Data mining

Mapping of events

and drugs

Development of

extraction tools

Mapping web page to Semantics

• named entity recognition on the

fly, mapping term variants to

same concept

• disambiguation on the fly using

context

• identifying semantic

relations/triples relevant for

user

• identifying most relevant

entities on a page

• showing additional information

in text

celecoxib causes upper gastrointestinal hemorrhage

celebrex causes Upper Gastro-Intestinal Bleeding Triple store

RDF/OWL

Adding semantics

Health Users

John has already been using celebrex for a few years to

kill the pain of his reumatoïde artritis.

Using the semantic layer he now nows that celebrex is the

brand name for celecoxib which belongs to the family of

nons-steroidal anti-inflammatory drugs.

This family of drugs is known to cause upper gastro-

intestinal bleedings. He will ask his general practitioner

whether there are alternatives that don’t have these

particular side effects.

Requirements

A rich enough ontology and triple store that connects topics

On the fly analysis of web pages to identify health topics

Term variations

Disambiguation / page analysis

Bench marking (CALBC, I2B2, BioCreative, TREC)

Linkage with different information sources

Information available at the point of reading

Semantic Enrichment

Easy deployment

Enrichment provided by site

On demand enrichment

User monitoring / intelligent systems

Based on context highlight different topics

Populating relevant linked information, depending on context

Client-side user tracking to determine context

Business model

Advertisement

Licensing by site owner

Licensing by end-user (app store)

Open (source) architecture

Next…

Extending Context specific population of linked information

Drugs -> side effects

Disease -> treatments, guidelines

Linking with online electronic health records

Linking with social media (patient organizations, patients with same

disease, patients like me)

Deeper NLP

Thanks for your attention!

I’m happy to take questions either now, if time permits,

or per e-mail: e.vanmulligen@erasmusmc.nl