II-SDV 2015, 20 - 21 April, in Nice

Text Mining: It's About Time
Andrew Hinton & David Milward
II-SDV, Nice France 20th April 2015

Overview: Text Mining, It's About Time !
Introducing text mining
Why text mining has now come of age
Speed to insight
Real time data & timeliness of data
Temporal nature of data
Future challenges
© Linguamatics 2015

What is Text Mining?
"Text Mining is the discovery by computer of
new, previously unknown information, by
automatically extracting and relating
information from different written resources,
to reveal otherwise "hidden" meanings. A
key element is the linking together of the
extracted information to form new facts or
new hypotheses to be explored further by
more conventional means of
experimentation."
Marti Hearst, UC Berkeley

How is it being used in Life Sciences?
Advanced text analytics delivers value along the pipeline
Gene-disease
mapping
Target ID/selection
Mutation/expression
analysis
Toxicity analysis and
prediction
Biomarker discovery
Drug repurposing
Patent analysis
KOL identification
Opportunity scouting
Trial site selection and study design
Safety
Competitive intelligence
Pharmacovigilance
Social media
analysis
Comparative Effectiveness
Regulatory Submission QC
HEOR
SAR

Challenges
Most of the information required is in free text
Ever-increasing amounts of text data to
examine
0
5.000.000
10.000.000
15.000.000
20.000.000
25.000.000
PubMed Records
− Different kinds of
documents
− External literature,
patents, EHRs, internal
reports, blogs,
presentations
− Different formats
− HTML, PDF, XML, Word,
PPT, Wiki

Challenges in Unstructured Data
Different word, same
meaning
cyclosporine
ciclosporin
Neoral
Sandimmune
Different expression, same
meaning
Non-smoker
Does not smoke
Does not drink or smoke
Denies tobacco use
Different grammar, same
meaning
5mg/kg of cyclosporine per day
5mg/kg per day of cyclosporine
cyclosporine 5mg/kg per day
Same word, different
context
Diagnosed with diabetes
Family history of diabetes
No family history of diabetes
NLP

From Words to Meaning
“Among them, nimesulide, a selective COX2 inhibitor, …”
Entrez Gene ID:
5743
inhibits
Entrez Gene ID: 5743
inhibits
Identifying
entities and
relations
Linguistics to establish relationships

Finding Indirect Relationships
Treatment has been applied in clinical trials
8
Thalidomide in advanced hepatocellular
carcinoma as antiangiogenic treatment
approach: a phase I/II trial.
Pinter et al.
Eur J Gastroenterol Hepatol. 2008
20(10):1012-9
Phase II study of temozolomide,
thalidomide, and celecoxib for newly
diagnosed glioblastoma in adults.
Kesari et al.
Neuro Oncol. 2008 10(3):300-8
<Thalidomide>-<Relationship>-<Gene> <Gene>-<Relationship>-<Angiogenic Process>

Modes of Use
© Linguamatics 2015 - Confidential
Reusable
pipelines
•Decision
support
•Knowledge
capture
•Classification/
mark-up
•Capture and
re-use
strategies
•Semantic
categorization
9

Speed to Insight

Time = Money

Speed to insight Example I: Patents

Business Impact and value

Leveraging Text Analytics in Patents to
Empower Business Decisions

Not for
profit
Education
Research Biotech
Pharma
Medical
devices
ICT
Funder
s
Approvers
Government
Patient
Payers
Prescribers
Providers
Dispensers
Electronic
Health
Record
EHRs & Healthcare Challenges
The challenge is
to unlock the
value of the huge
investment being
made in EHRs
“Natural language processing (NLP)
and visualization dashboards are the technologies most
suitable to improve EHR usability. NLP can produce
readable summaries of unstructured text, helping
clinicians retrieve information needed for point-of-care
decision making”
Frost and Sullivan, 2014

CHALLENGE
Identifying disease
comorbidities for study via
patient narratives and
disease codes is often
slow and manual. To find
700 patients with HIV and
Hepatitis C took 5 medical
students 4 months.
SPEED TO INSIGHT EXAMPLE
COHORT SELECTION
MINING PATIENT RECORDS FOR DISEASE COMORBIDITIES
SOLUTION
Using text mining queries
for disease codes and
terminology took less than
half a day to identify 1100
patients.
BENEFIT
Patient groups can be
quickly identified from
both structured and
unstructured text.
Identifying new disease
cohorts is easy and can be
quickly iterated to select
new groups for study.

Real time data & timeliness of data

Patent Analytics with I2E
Comprehensive Effective Search For Patent
Landscaping
CHALLENGE
Patents are a valuable
source of novel data.
Identifying drug targets
for specific indications is
often slow and manual, as
patents are long and the
language obtuse. To find
targets for 3 therapeutic
areas took 50 FTE days.
SOLUTION
A pipeline was built that
used queries to extract
target, indication,
invention type and
organisations and feed
into a database. Recall
was 10x manual, with
good precision; plus
target relevance scores.
BENEFIT
The integrated process
drastically reduces the
FTEs required to keep the
organization up-to-date
on recent findings
published in the patent
literature.

Temporal Nature of Data

Diagnose Cancer Earlier: Pulmonary Nodule
Early diagnosis of lung cancer is limited because predictive
models rely on a combination of structured and textual data
For example:
Cancer Risk
Low Intermediate High
Nodule size, diameter (mm) <8 8 to 20 >20
Age, yr <45 45 to 60 >60
Prior cancer history No prior cancer Prior cancer history
Tobacco use (pack/day) Never smoked 1 >1
Smoking cessation Quit > 7 yr ago Quit <7 yr ago Never quit
Chronic obstructive lung disease No COPD COPD
Asbestos exposure No exposure Exposure
Nodule characteristics Smooth Lobulated Spiculated

Temporal Nature of Data
What is the challenge
− Temporal attributes of an individual “event” e.g.
cancer ‘v’ previous history of cancer (before ‘v’ after)
− Emerging hypotheses e.g. “X may represent a novel
technique for Y”
− Temporal nature of corpora e.g. Published literature-
Grants-Patents
Examples
− I2B2 Challenge
− Opposition based searching
− Patents-NIH-Grants-Medline

I2B2 2014 Cardiac Risk Factors
The challenge is to extract a fixed set of Cardiac Risk
factors
Risk factors include:
− medications, mentions of diabetes, hypertension,
hyperlipidaemia, obesity, glucose/LDL/A1C/BMI test results,
“cardiac events”, family history of Coronary Artery Disease,
smoking etc.
Each annotation must also be given a temporal relation
to the document i.e.
− the patient had a heart attack BEFORE the day of the report
− the patient’s LDL was tested DURING the day of the report
There might be multiple annotations if the risk factor is
ongoing
− Diabetes is probably going to be BEFORE, DURING and AFTER
Precision: 89.8%, Recall: 93.8%, F1-score: 91.7%

Key Insights: Temporal Data
Events tend to have a "default" time if no appropriate language or
dates are mentioned
− "Medications: Metformin, Aspirin" -> presumed to be continuing
Language to express temporal relations also depends on what you are
trying to extract
− "Patient discontinued metformin" -> the patient took the drug
before the report but is not continuing it
− "Starting a course of metformin" -> the patient will start the
course after the report but did not take it before the report
− "Avoid metformin" -> the patient will stop taking the drug but
took it before the report
− "Patient had Myocardial Infarction this morning" -> use pronouns
to establish relation to report (on the date the report was written)
− "previous A1c was 6.5" -> use temporal adjectives and the tense
of the verb for test results

Key Insights: Temporal Data
Reports are often written after the event was described,
however, so you can't always rely on the tense of the verb
− "Her BP today was 120/90"
Extracting a date within a few words of the event often
implies the event took place in the past
− "10/12 Pt brought in after Myocardial Infarction"
− "LDL from 10/11/09 120"

Opposition Searching
Single search over multiple data on different
servers providing a single set of results
Information from differently structured data is
brought together and ordered by year

Connected Data Technology
Single Query over
Multiple Data Sources and Network Locations

Challenges: A Big Data Future
High indexing performance
• Millions of documents, TBs of storage
• Ontologies with 100,000s of terms
• Handles large documents with ease
• Open, configurable pipeline
• Advanced table processing
VOLUME VARIETY VELOCITY VISUALIZATION
Connected data technology
• Unified heterogeneous document types across federated servers
• Connect – Normalize – Use
• Structured, semi-structured and unstructured
Distributed indexing and querying
• Multi-processor
• Multi-machine
Integrates in enterprise applications, portals, pipelines and workflows
• Open web services API
• Public query language
Strong integrated visualization

Conclusion: Text mining; It’s About Time To Start Using It !
Use of text mining demonstrates clear value in
the Pharma & Healthcare sectors
− Time to insight
− Timeliness of data
− Real time data
− Temporal data
Technology improvements make possible real-
time, Text mining that is both agile and scalable
in a world of big Data

Question Time
Thanks to :
David Milward, James Cormack, Jane Reed, Simon Beaulah & Phil Hastings

Linguamatics Text Mining Summit
October 12-14 2015, Newport RI
www.linguamatics.com/textminingsummit
Featuring customer use cases in the life sciences and healthcare, hands-on training,
and new healthcare hackathon

II-SDV 2015, 20 - 21 April, in Nice

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (16)

Similar to II-SDV 2015, 20 - 21 April, in Nice

Similar to II-SDV 2015, 20 - 21 April, in Nice (20)

More from Dr. Haxel Consult

More from Dr. Haxel Consult (20)

II-SDV 2015, 20 - 21 April, in Nice