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  Pilot project  Knowledge-intensive NLP for clinical narrative  Current limitations  Identify physiologic functions only  In published clinical case reports [see CAS_report.pdf] CAS_report.pdf  Motivation for addressing clinical LOINC  Clinical observations less likely structured  LOINC standard for communicating observations  Side benefit  Possibility of semi-automated LOINC coding

Background  Previous published research  Terminologies mapped to LOINC  No mapping of documents to LOINC  Limitation of existing NLP methods  MetaMap: Interaction with LOINC in Metathesaurus [see CAS_examples.doc] CAS_examples.doc  String matching: LOINC specification not accommodated [see CAS_examples.doc] CAS_examples.doc  LOINC mapping is knowledge intensive

Methods: Overview  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] CAS_annotate.txt

Methods: Knowledge  Use canonical document structure  Physical examination section Begin: Lexical cues (e.g. examination) Begin: Lexical cues (e.g. examination) End: Semantic types (e.g. Diagnostic Procedure) End: Semantic types (e.g. Diagnostic Procedure)  Use UMLS Semantic types  To identify physiologic functions (Physiologic Function, Organism Function, Clinical Attribute, Organism Attribute)  Use syntactic context  Disambiguation: “BP” followed by quantitative concept

Methods: LOINC structure  Vital Signs  Blood pressure  system (4th field)=arterial  Respiratory rate  system=respiratory  Heart rate  system=XXX  Quantitative (QN) in 5th field (scale)  Choose most general LOINC  No periods in any field  No “^” (other than “^Patient”) in any field  No “difference” in 2nd field

Discussion  Preliminary results [see CAS_output.txt] CAS_output.txt  Initial phase  Assess feasibility  Note issues faced  Next Steps  Expand rules  Based on structured knowledge What information does LOINC encode What information does LOINC encode How is it represented 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 Mar- Apr;5(2): PMID: 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 Jan;42(1): PMID: 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 May-Jun;5(3): PMID: 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