Mapping Clinical Narrative to
LOINC: A Preliminary Report
Charles A. Sneiderman, M.D., Ph.D.
Marcelo Fiszman, M.D., Ph.D.
Honglan Jin, Ph.D.
Thomas C. Rindflesch, Ph.D.
Lister Hill National Center for Biomedical Communications
Introduction
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Pilot project
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Current limitations
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Identify physiologic functions only
In published clinical case reports [see CAS_report.pdf]
Motivation for addressing clinical LOINC
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Knowledge-intensive NLP for clinical narrative
Clinical observations less likely structured
LOINC standard for communicating observations
Side benefit
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Possibility of semi-automated LOINC coding
Background
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Previous published research
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Limitation of existing NLP methods
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Terminologies mapped to LOINC
No mapping of documents to LOINC
MetaMap: Interaction with LOINC in Metathesaurus
[see CAS_examples.doc]
String matching: LOINC specification not
accommodated [see CAS_examples.doc]
LOINC mapping is knowledge intensive
Methods: Overview
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MetaMap to UMLS first
 Then apply knowledge-based rules
 Extension of “Lexically Assign, Logically Refine”
strategy of Dolin et al. (1998)
 Evaluation against coding by FP (CS) checked by
Cardiologist (BB) [see CAS_annotate.txt]
Methods: Knowledge
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Use canonical document structure
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Physical examination section
•
•
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Use UMLS Semantic types
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Begin: Lexical cues (e.g. examination)
End: Semantic types (e.g. Diagnostic Procedure)
To identify physiologic functions (Physiologic
Function, Organism Function, Clinical Attribute,
Organism Attribute)
Use syntactic context
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Disambiguation: “BP” followed by quantitative concept
Methods: LOINC structure
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Vital Signs
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Blood pressure  system (4th field)=arterial
Respiratory rate  system=respiratory
Heart rate  system=XXX
Quantitative (QN) in 5th field (scale)
Choose most general LOINC
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No periods in any field
No “^” (other than “^Patient”) in any field
No “difference” in 2nd field
Discussion
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Preliminary results [see CAS_output.txt]
 Initial phase
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Assess feasibility
Note issues faced
Next Steps
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Expand rules
Based on structured knowledge
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•
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What information does LOINC encode
How is it represented
Exploit LOINC information model (Forrey et al. 1996;
Huff et al. 1998; McDonald et al. 2003)
References
Dolin RH, Huff SM, Rocha RA, Spackman KA, Campbell KE. Evaluation of a
“lexically assign, logically refine” strategy for semi-automated integration of
overlapping terminologies. J Am Med Inform Assoc. 1998 Mar- Apr;5(2):203- 13.
PMID: 9524353
Forrey AW, McDonald CJ, DeMoor G, Huff SM, Leavelle D, Leland D, Fiers T,
Charles L, Griffin B, Stalling F, Tullis A, Hutchins K, Baenziger J. Logical
observation identifier names and codes (LOINC) database: a public use set of
codes and names for electronic reporting of clinical laboratory test results. Clin
Chem. 1996 Jan;42(1):81-90. PMID: 8565239
Huff SM, Rocha RA, McDonald CJ, De Moor GJ, Fiers T, Bidgood WD Jr, Forrey
AW, Francis WG, Tracy WR, Leavelle D, Stalling F, Griffin B, Maloney P,
Leland D, Charles L, Hutchins K, Baenziger J. Development of the Logical
Observation Identifier Names and Codes (LOINC) vocabulary. J Am Med Inform
Assoc. 1998 May-Jun;5(3):276-92. PMID: 9609498
McDonald CJ, Huff SM, Suico JG, Hill G, Leavelle D, Aller R, Forrey A, Mercer K,
DeMoor G, Hook J, Williams W, Case J, Maloney P. LOINC, a universal standard
for identifying laboratory observations: a 5-year update. Clin Chem. 2003
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