MEDLEE: natural language processing on the public health grid

© Columbia University
MedLEE, a Natural Language
Processing Service, on the Public
Health Research Grid
Albert Lai, Ph.D.
Carol Friedman, Ph.D.
Department of Biomedical Informatics,
Columbia University

Goals
 Better understand grid technology
 Provide NLP service to members of the
Public Health Research Grid

Motivation
 Coded information is urgently needed for
clinical applications that improve care and
lower costs
 Coded data other than laboratory data and
pharmacy data - very scarce and difficult
to obtain

Why NLP?
 NLP technology offers high throughput
method for automatic encoding of clinical
information in narrative patient reports
 Applicable to a broad range of clinical
domains
 Appropriate for diverse applications

Outline
 MedLEE Overview
 Getting on the Grid
 Service Generalization Issues

MedLEE Overview
 Medical Language Extraction and
Encoding
Extracts, structures, and encodes clinical
information in narrative patient reports
Comprehensive coverage
Can be used for diverse clinical applications
Development started in 1991
Used at Columbia University Medical Center
since 1995
Numerous independent evaluations

....New maculopapular rash
on trunk ….
MedLEE
Problem:rash
Status:new
Descriptor:maculopapular
Bodyloc:trunk
Code:C0460005 (trunk)
Code:C0241488 (trunk
maculopapular rash)
Analytics
Patient report
Coded data
Clinical Guidance
-- Indicate potential notifiable
disease for reporting.
-- Inform of local outbreak and
indicate appropriate tests.
-- Indicate need for vaccination.
Surveillance
-- Indicate potential bioterrorist
event.
-- Transmit syndromic event to
health dept for surveillance.
Quality Assurance
Detect potential cases of
medication reaction.
Clinical Research
-- Detect cases of rash for
inclusion in trial of new
treatment.
-- Find genetic associations
with atopic rash.
Applications

MedLEE Architecture
Text
Report
MedLEE Application
Postprocessor
Preprocessor
standardize
Input to MedLEE
transform output
for application

Text Reports Processed
 Radiology Reports
 Cardiology Reports
 Pathology Reports
 Admission notes
 Discharge Summaries
 Resident Sign out notes
 Office Visits
 Telephone encounters

Applications using MedLEE
 Biosurveillance
 Syndromic surveillance
 Adverse Drug Event detection
 Decision Support
 Clinical Research
 Clinical Trials
 Quality Assurance
 Automated Encoding
 Patient Management
 Data mining – finding trends and associations
 Linking patient record to the literature
 Summarization

Adverse Event Detection
 Use NLP system to detect events defined in the
New York Patient Occurrence Reporting and
Tracking System (NYPORTS)
 System: MedLEE +
 Queries consisiting of criteria mapping output to
NYPORTS event
 Results: system outperformed traditional and
previous automated methods  NLP effective
method for automated adverse event detection
Melton GB, Hripcsak G. Automated detection of adverse events using natural language processing of discharge summaries.
J Am Med Inform Assoc. 2005 Jul-Aug;12(4):448-57.

Syndromic Surveillance
 EHR from Institute of Family Health (IFH)
 13 community health centers
 System: MedLEE+queries detecting flu-like
illness
 Processed all types of notes:
 nursing and physician notes
 several types of encounters
 Results:
 System correlated well with a proven syndromic
surveillance system based on chief complaints

Challenges for Grid-enabling MedLEE
 Setting up a grid node
 Firewalls
 Security / PHI concerns
 Wrapping MedLEE into a grid service

Getting on the Grid
 CUMC on hospital network
Setting up Globus node required numerous
port openings
 No DMZ available
 Solution: install main campus w/ no
firewall

Security Concerns
 No firewall
 Medical data being sent to node
 Medical data remains on node for some
period of time
 Currently, we do not send data containing
PHI to the Grid service, only in
demonstration mode

Wrapping MedLEE
 Introduce framework from caGrid / OSU
 Using schema for web service,
automatically generates custom beans
 Automatically generates method stubs for
service operations
 Can configure security parameters for
service

MedLEE Example
Pre-
processor
[new,maculopapular,rash,on,trunk,’.’]
Parser [problem,rash,[status,new],[descriptor,maculopapular],[bodyloc,tru
nk]]
Encoder [problem,rash,[status,new],[descriptor,maculopapular],[bodyloc,tru
nk,[code,C0460005^trunk]],[code,C0241488^trunk maculopapular
rash]
XML
Translator
<problem v = “rash”>
<status v = “new”></status>
<descriptor v = “maculopapular”></descriptor>
<bodyloc v = ”trunk”>
<code v = “C046005^trunk”</code>
</bodyloc>
<code v = “C0241488^trunk maculopapular rash” </code>
</problem>
“New maculopapular rash on trunk.”

Service Generalization Issues
 Preprocessing needed to standardize
narrative prior to MedLEE
 Allow user-specified customization

Preprocessor – Standardize Report for
MedLEE
 Handle problematic text (formatted data in narrative)
Her2/Neu-2+  Her2/Neu measured 2+.
 Add punctuations (e.g., ‘.’) for run on sentences
 Add ignore tags to indicate text that should be ignored
This neoplasm is strongly estrogen positive <ign> To
date an indicator reliable in predicting tumor recurrence
in node-negative patients is not available. </ign>
 Add tags from external processor
The patient had fever <phr sem=“date” t=“19991002”>2
days before admission</phr>

Postprocessor – Transform output
 Modify and transform output for different
views/applications
Map into tabular form for
spreadsheet/database
Problem-oriented/summarization/highlighting
Execute queries to interpret output according
to needs of application

User-Specified Knowledge
 Section headings
New sections/abbreviated section headers:
 PMH|Past Medical History
 New abbreviations containing ‘.’ (O.D.)
 Avoids breaking up sentence at abbreviation
 Add/replace lexical definitions
 OD|bodyloc|right eye

Example of Need for Custom Lexicon
 “27 yo male no sig PMH presented to ED
on 4/23 w/ fevers, backache,headache,
pharyngitis, no rhinorrhea, no cough,
lasting 1 day; Now sxs improved.”
 Add/replace lexical definitions
 ed|service|emergency department
 sxs|cfinding|symptom

finding:demo
age>> [27,[idref,2],year,[idref,4]]
parsemode>> mode2
sectname>> report clinical information item
sex>> male
idref>> 6
sid>> 1
status:past medical history
certainty>> no
idref>> 8
idref>> 12
timeper>> presentation
idref>> 14
parsemode>> mode4
sid>> 1
.....
finding:better
idref>> 63
parsemode>> mode4
sid>> 2
timeper>> now
idref>> 59
finding:demo
age>> [27,[idref,2],year,[idref,4]]
parsemode>> mode2
sex>> male
idref>> 6
sid>> 1
status:past medical history
certainty>> no
idref>> 8
idref>> 12
timeper>> presentation
idref>> 14
service>> emergency department
idref>> 18
location>> to
idref>> 16
date>> 00000423
idref>> 22
parsemode>> mode4
sid>> 1
…..
problem:symptom
change>> better
idref>> 63
idref>> 61
parsemode>> mode1
sid>> 2
timeper>> now
idref>> 59

Where are we at now?
 Basic MedLEE node
 Encrypted data transport using TLS
 Authentication using gridmap file

Next Steps
 Addition of customization features to
GridMedLEE for greater generalization
User-defined / domain-specific lexicons,
terminology, section headings, etc.
Additional output formats
 Investigate other security mechanisms
 Investigate real usage on grid in research
applications

Acknowledgments
 Ken Hall
 Dan Washington
 Brian Lee
 Shannon Hastings
 Philip Payne
This work was supported by Centers for Disease Control
and Prevention grant P01 HK000029

MEDLEE: natural language processing on the public health grid

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