UMLS CONCORDANCE FOR A
COMPREHENSIVE PATHOLOGY TEXT.

John H. Sinard, MD, PhD [1].
G. William Moore, MD, PhD [2,3,4].

From: Departments of Pathology, Yale University School of Medicine, New Haven, CT [1];
Baltimore VA Medical Center [2],
University of Maryland School of Medicine [3],
and The Johns Hopkins Medical Institutions, Baltimore, MD [4].

U. S. Government Work, published in:
the Johns Hopkins Autopsy Resource,
www.netautopsy.org

TABLE OF CONTENTS.


1. ABSTRACT.
2. INTRODUCTION.
3. DESIGN.
4. UNIFIED MEDICAL LANGUAGE SYSTEM.
5. REDUNDANT INDEXING OF SUBCONCEPTS.
6. BARRIER WORD METHOD.
7. BARRIER WORD METHOD: SAMPLE TEXT.
8. AMBIGUITIES IN UMLS.
9. RESULTS.
10. DISCUSSION.
11. FUTURE DIRECTIONS: SYNONYMS FOR UMLS.
12. REFERENCES.
13. ZIPF DISTRIBUTION OF UMLS CUIS.
14. ZIPF DISTRIBUTION OF UNMATCHED WORDS.
15. ZIPF DISTRIBUTION OF COLLOCATIONS.
16. ADDITIONAL REFERENCES.

1. ABSTRACT.

UMLS CONCORDANCE FOR A
COMPREHENSIVE PATHOLOGY TEXT.


NEXT PAGE.
RETURN TO TABLE OF CONTENTS.
John H. Sinard, MD, PhD [1].
G. William Moore, MD, PhD [2,3,4]. From: Department of Pathology, Yale University School of Medicine, New Haven, CT [1];
Pathology and Laboratory Medicine Service (113) Baltimore VA Medical Center [2],
Department of Pathology, University of Maryland School of Medicine [3],
Department of Pathology, The Johns Hopkins Medical Institutions, Baltimore, MD [4].

      Background: The Unified Medical Language System (UMLS) of the U. S. National Library of Medicine is the world's largest system of medical concepts, with over 700,000 concept-unique-identifers (CUIs), 1.5 million synonyms, and partial translations into over twenty languages in the year 2000 edition. We sought to determine the inclusiveness of the UMLS for concepts in pathology by building a concordance to a popular review text in pathology.

      Design: A distribution of single words, as well as multiple-word terms (collocations), was obtained for the electronic text of Sinard's Outlines in Pathology by the Barrier Word Method, a method employed in automated Medline indexing. Exact matches were made to the synonym-field in the UMLS Metathesaurus, and additional approximate matches were obtained manually.

      Results: The input text was 951 KB, with 120,677 words, 11,240 of them distinct, ranging in frequency from 4,037 occurrences of 'of', to 4,512 words occurring only once, an average of 10.7 = 120,677/11,240 occurrences per word. There were 3,520 distinct collocations with exact or approximate UMLS matches. There were 77,498 (64.2%) exact matches to a UMLS synonym, and 33,348 (27.6%) additional, approximate matches to UMLS CUIs, resulting in 8.1% unmatched concepts.

      Conclusion: Results suggest that UMLS is a highly inclusive concept system for human pathology, with 91.9% exactly or approximately matched concepts in a comprehensive pathology outline. However, the UMLS is synonym-poor, and many synonyms must be added manually to accommodate pathology free-text. Thus, the UMLS appears to be a sufficiently rich concept system for inter-institutional exchange of pathology data.

2. INTRODUCTION.


NEXT PAGE.
PREVIOUS PAGE.
RETURN TO TABLE OF CONTENTS.


  • UNIFIED MEDICAL LANGUAGE SYSTEM METATHESAURUS (UMLS-M) OF THE U. S. NATIONAL LIBRARY OF MEDICINE (USNLM).

  • MOST COMPREHENSIVE, PUBLICLY-AVAILABLE LIST OF STANDARDIZED MEDICAL TERMINOLOGY IN THE WORLD.

  • WHAT IS THE CONCORDANCE RATE FOR GENERAL PATHOLOGY TEXT?


    • 3. DESIGN.


    NEXT PAGE.
    PREVIOUS PAGE.
    RETURN TO TABLE OF CONTENTS.


  • SINARD'S OUTLINES IN PATHOLOGY. 25 CHAPTERS, POPULAR REVIEW TEXT FOR PATHOLOGY BOARDS.

  • ALL MAJOR AREAS OF ANATOMIC PATHOLOGY ARE COVERED.

  • COMPUTER-ENCODED INTO UMLS, WITH ENRICHED SYNONYM LIST.

  • CONCORDANCE: MEDICALLY-SIGNIFICANT TERM, PRESENT IN THE TEXTBOOK, AND ALSO CAPTURED BY ENCODING PROGRAM.

  • FALSE NEGATIVE: UMLS CONCEPT NOT PRESENT.

  • AMBIGUOUS TERMS AND COMPOUND TERMS CONTAINING SUBCONCEPTS INDEXED REDUNDANTLY.


    • 4. UNIFIED MEDICAL LANGUAGE SYSTEM (UMLS).


    NEXT PAGE.
    PREVIOUS PAGE.
    RETURN TO TABLE OF CONTENTS.


  • UNIFIED MEDICAL LANGUAGE SYSTEM (UMLS) : DEVELOPED BY U.S. NATIONAL LIBRARY OF MEDICINE (USNLM) IN 1986.

  • PURPOSE: AID DEVELOPMENT OF SYSTEMS TO RETRIEVE ELECTRONIC BIOMEDICAL INFORMATION.

  • http://www.nlm.nih.gov/research/umls/

  • LAST UPDATED: January 1, 2000.

  • METATHESAURUS SIZE: 113,699,627 BYTES.

  • CONCEPT UNIQUE IDENTIFIERS (CUIs): 729,248, MAX=C0813178, RETIRED=83,930.

  • SYNONYMS: 1,598,176

  • OVER 50 SOURCE-VOCABULARIES.

  • OVER 20 PARTIAL TRANSLATIONS INTO FOREIGN LANGUAGES.


    • 5. REDUNDANT INDEXING OF SUBCONCEPTS.


    NEXT PAGE.
    PREVIOUS PAGE.
    RETURN TO TABLE OF CONTENTS.

    CELLULAR BLUE NEVUS REDUNDANTLY INDEXED AS:


  • CELLULAR BLUE NEVUS (C0334448).

  • BLUE NEVUS (C0206736).

  • CELL (C0007634).

  • BLUE (C0332584).

  • NEVUS (C0027960).


    • 6. BARRIER WORD METHOD.


    NEXT PAGE.
    PREVIOUS PAGE.
    RETURN TO TABLE OF CONTENTS.


  • NATURAL-LANGUAGE MEDICAL TEXT: SEQUENCE OF MEDICAL CONCEPTS SEPARATED BY GRAMMATICAL OBJECTS.

  • THE GRAMMATICAL OBJECTS, OR BARRIER WORDS: NUMERALS, PUNCTUATION, SINGLE LETTERS, ARTICLES, PREPOSITIONS, AND COMMON VERBS AND MODIFIERS.

  • MEDICAL CONCEPTS, OR KEYWORDS: ARE ONE-WORD OR MULTIPLE-WORD TERMS CONSISTING OF MEDICALLY SIGNIFICANT WORDS.


    • 7. BARRIER WORD METHOD: SAMPLE TEXT.


    NEXT PAGE.
    PREVIOUS PAGE.
    RETURN TO TABLE OF CONTENTS.
    LICHEN SIMPLEX CHRONICUS . CHRONIC FORM of any of above with IRRITATION and TRAUMA . EPIDERMIS undergoes a PSORIASIFORM THICKENING but with an increased THICKNESS of the GRANULAR LAYER . SCARRING and BROADENING of DERMAL PAPILLAE .


  • barrier words in lower case.

  • KEYWORDS IN UPPER CASE. 


    •                   TEXT NAME            UMLS CUI
   LICHEN SIMPLEX CHRONICUS            C0149922
               CHRONIC FORM   C0205179 C0376315
                         of            C0456627
                        any            C0205392*
                         of            C0456627
                      above            C0205103
                       with            C0332287
                 IRRITATION            C0441718
                        and            C0332287*
                     TRAUMA            C0548346
                  EPIDERMIS            C0014518
                  undergoes
                          a            C0205447*
    PSORIASIFORM THICKENING  C0033860* C0332527
                        but            C0332287*
                       with            C0332287
                         an            C0205447*
                  increased            C0205216
                  thickness            C2005400
                         of            C0456627
                        the            C0205435*
             GRANULAR LAYER   C0205247 C0205274*
                   SCARRING            C0036287
                        and            C0332287*
                 broadening            C0332464*
                         of            C0456627
            DERMAL PAPILLAE   C0221927 C0205312*





    8. AMBIGUITIES IN UMLS.


    NEXT PAGE.
    PREVIOUS PAGE.
    RETURN TO TABLE OF CONTENTS.


  • ADNEXA WITHOUT NEARBY DISAMBIGUATING WORD:


  • SKIN ADNEXA (C0221943)

  • UTERINE ADNEXA (C0001575)

  • OCULAR ADNEXA (C0229243)


    • 9. RESULTS.


    NEXT PAGE.
    PREVIOUS PAGE.
    RETURN TO TABLE OF CONTENTS.


  • INPUT TEXT: 951 KB, 25 CHAPTERS.

  • 120,677 WORDS, 11,240 DISTINCT WORDS,

  • FROM 4,037 OCCURRENCES OF 'OF', TO 4,512 WORDS OCCURRING ONLY ONCE.

  • AVERAGE: 10.7 = 120,677/11,240 OCCURRENCES PER WORD.

  • 3,520 DISTINCT COLLOCATIONS WITH EXACT OR APPROXIMATE UMLS MATCHES.

  • 77,498 (64.2%) EXACT MATCHES TO A UMLS SYNONYM, AND 33,348 (27.6%) ADDITIONAL, APPROXIMATE MATCHES TO UMLS CUIS,

  • 8.1% UNMATCHED CONCEPTS.


    • 10. DISCUSSION.


    NEXT PAGE.
    PREVIOUS PAGE.
    RETURN TO TABLE OF CONTENTS.


  • CONCORDANCE RATE: 90.9%.

  • UNMATCHED CONCEPTS TENDED TO BE DESCRIPTIVE TERMS IN PATHOLOGY THAT CHARACTERIZE MICROSCOPIC FINDINGS.

  • UMLS IS A HIGHLY INCLUSIVE CONCEPT SYSTEM FOR PATHOLOGY.

  • HOWEVER, UMLS IS SYNONYM-POOR.

  • MANY SYNONYMS MUST BE ADDED MANUALLY.

  • UMLS: NEARLY-COMPREHENSIVE METATHESAURUS FOR PATHOLOGY TEXT.


    • 11. FUTURE DIRECTIONS:
    SOURCES FOR SYNONYMS.


    NEXT PAGE.
    PREVIOUS PAGE.
    RETURN TO TABLE OF CONTENTS.


  • LEXICAL VARIANTS: NUCLEI ==> CELL NUCLEUS.

  • OBVIOUS SYNONYMS: CLUSTER ==> AGGREGATE.

  • OBVIOUS MISSPELLINGS: WILM'S ==> WILMS'.
    BRONCHITS ==> BRONCHITIS.

  • OBVIOUS CONTRACTIONS: ADDISON ==> ADDISON'S DISEASE.
    CUSHING ==> CUSHING'S DISEASE.
    SQUAMOUS ==> SQUAMOUS CELL.

  • COMPOUNDS: WITHOUT ==> NEGATIVE-WITH.


    • 12. REFERENCES.


    NEXT PAGE.
    PREVIOUS PAGE.
    RETURN TO TABLE OF CONTENTS.

  • 1. UMLS Knowledge Sources. 11th edition. 2000. Documentation. National Institutes of Health. National Library of Medicine. Bethesda, Maryland 20854.

  • 2. College of American Pathologists. Systematized Nomenclature of Human and Veterinary Medicine (SNOMED International). College of American Pathologists, Northfield, IL, 1993.

  • 3. Moore GW, Berman JJ, Hanzlick RL, Buchino JJ, Hutchins GM.
    A prototype Internet autopsy database. 1625 consecutive fetal and neonatal autopsy facesheets spanning 20 years.
    Arch Pathol Lab Med. 1996 Aug;120(8):782-785.

  • 4. Moore GW, Miller RE, Hutchins GM. Indexing by MeSH titles of natural language pathology phrases identified on first encounter using the barrier word method.
    In: Scherrer JR, Côté RA, Mandil SH, eds. Computerized Natural Medical Language Processing for Knowledge Representation. Amsterdam: North-Holland; pp 29-39, 1989.

  • 5. Sinard JH.
    Outlines in Pathology.
    Philadelphia: WB Saunders. 1996.


    13. ZIPF DISTRIBUTION OF UMLS CUIS.


    NEXT PAGE.
    PREVIOUS PAGE.
    RETURN TO TABLE OF CONTENTS. 

       RANK  FREQUENCY                WORD       UMLS CUI 
      1      3,950                  of       C0456627
      2      2,591                  in       C0439203
      3      2,387                 and       C0332287*
      4      1,873                with       C0332287
      5      1,779                  to       C0332285*
      6      1,562                 the       C0205435*
      7      1,297                  or       C0332270*
      8      1,256               cells       C0007625
      9        904             usually       C0332183*
     10        899                cell       C0007634
     11        847                 may       C0806904
     12        711                  be       C0014121
     13        682                  by       C0336807
     14        681                most       C0205381
     15        604                 are       C0392148*
     16        537              common       C0205213
     17        521                  is       C0441912
     18        469               often       C0332181
     19        446                 can       C0808716
     20        439               tumor       C0027651
     21        435                 for       C0521117
     22        433               small       C0700320
     23        418                from       C0332285*
     24        406             disease       C0012633
     25        384                 but       C0332287*
     26        383           carcinoma       C0007095
     27        369                 not       C0205160*
     28        364                more       C0205171
     29        358                seen       C0205395
     30        344              tumors       C0027651
     31        334               large       C0549176
     32        333                type       C0332307
     33        322                 aka       C0332287*
     34        321                have       C0605770*
     35        307                  at       C0332285*
     36        298                  on       C0332285* 
     37        294                  as       C0003818
     38        269               which       C0043237*
     39        267                  no       C0205160*
     40        263              tissue       C0040300
     41        254            patients       C0030704
     42        248           malignant       C0205282
     43        245             present       C0392743
     44        242          associated       C0004083*
     45        240                also       C0332287*
     46        236             chronic       C0205179
     47        234                 all       C0444867
     48        232             lesions       C0221198
     49        226           prognosis       C0220901
     50        222                 age       C0001774



    14. ZIPF DISTRIBUTION
    OF UNMATCHED WORDS.


    NEXT PAGE.
    PREVIOUS PAGE.
    RETURN TO TABLE OF CONTENTS. 

       RANK  FREQUENCY                WORD
      1         48                cord      
      2         33               still      
      3         30          eventually      
      4         30                need      
      5         28               cords      
      6         27          palisading      
      7         27        particularly      
      8         26                must      
      9         25               plump      
     10         24           polygonal      
     11         24           represent      
     12         24             sharply      
     13         23         counterpart      
     14         23                germ      
     15         23                host      
     16         22       immunoblastic      
     17         22              remain      
     18         22              should      
     19         22             undergo      
     20         21              mantle      
     21         21          parenchyma      
     22         19                just      
     23         19               prone      
     24         19             unclear      
     25         18               villi      
     26         17      subendothelial      
     27         16               amino      
     28         16            arranged      
     29         16          background      
     30         16           excellent      
     31         16        intrahepatic      
     32         16         odontogenic      
     33         15              excess      
     34         15               glans      
     35         15              goblet      
     36         15                half      
     37         15           untreated      
     38         15             villous      
     39         14         independent      
     40         14               laden      
     41         14             outflow      
     42         14            subtypes      
     43         13             bundles      
     44         13              entity      
     45         13        extrahepatic      
     46         13      granulomatosis      
     47         13    intracytoplasmic      
     48         13          invariably      
     49         13           oncocytic      
     50         13          perineural



    15. ZIPF DISTRIBUTION OF COLLOCATIONS.


    NEXT PAGE.
    PREVIOUS PAGE.
    RETURN TO TABLE OF CONTENTS. 

       RANK  FREQUENCY                   TERM     UMLS CUI 
      1        479                 of the     C0332285*
      2        244                 in the     C0332285*
      3        196        associated with     C0332281
      4        101                 due to     C0678226
      5         86            giant cells     C0017526
      6         82           plasma cells     C0032112
      7         78          smooth muscle     C0026843
      8         54            tumor cells     C0431085
      9         50                 type i     C0441729
     10         49     autosomal dominant     C0443147
     11         49             clear cell     C0229473
     12         49    well differentiated     C0205615
     13         44               into the     C0332285*
     14         44                type ii     C0441730
     15         41            soft tissue     C0225317
     16         39               gi tract     C0017189
     17         38       germinal centers     C0282491
     18         38              low grade     C0205080
     19         37             absence of     C0332197
     20         37    autosomal recessive     C0441748
     21         35      connective tissue     C0009780
     22         35    squamous metaplasia     C0025570
     23         34           spindle cell     C0682540
     24         34          squamous cell     C0221910
     25         33             bile ducts     C0005400
     26         33             within the     C0332285*
     27         32   chronic inflammation     C0021376
     28         32               from the     C0332285*
     29         31                foci of     C0205234
     30         31         poor prognosis     C0278252
     31         31            rather than     C0489693*
     32         31           well defined     C0442825
     33         30 differential diagnosis     C0220820
     34         30             giant cell     C0017526
     35         28      basement membrane     C0004799
     36         28         good prognosis     C0278250
     37         28             high grade     C0205082
     38         28          renal failure     C0035078
     39         27            bone marrow     C0005953
     40         27                in situ     C0444498
     41         26              bile duct     C0005400
     42         26            lymph nodes     C0154054
     43         26squamous cell carcinoma     C0007137
     44         25   rheumatoid arthritis     C0003873
     45         24              cell type     C0449475
     46         24           soft tissues     C0225317
     47         23               but also     C0332287*
     48         22          blood vessels     C0005847
     49         22     inflammatory cells     C0440752
     50         22               of these     C0332285*


    16. ADDITIONAL REFERENCES.


    PREVIOUS PAGE.
    RETURN TO TABLE OF CONTENTS.

    Berman JJ.
    Biomedical Informatics.
    Sudbury, MA: Jones & Bartlett Publishers. 2006;:.
    ISBN-10: 0763741353, 459 pages.
    ISBN-13: 978-0763741358, 459 pages.
    http://www.julesberman.info/resource.htm

    Berman JJ, Albores-Saavedra J, Bostwick D, Delellis R, Eble J, Hamilton SR, Hruban RH, Mutter GL, Page D, Rohan T, Travis W, Henson DE.
    Precancer: a conceptual working definition -- results of a Consensus Conference.
    Cancer Detect Prev. 2006;30(5):387-394. Epub 2006 Oct 31.
    PMID: 17079091
    PubMed Entry

    McDonald CJ, Dexter P, Schadow G, Chueh HC, Abernathy G, Hook J, Blevins L, Overhage JM, Berman JJ.
    SPIN query tools for de-identified research on a humongous database.
    AMIA Annu Symp Proc. 2005;:515-519.
    PMID: 16779093
    PubMed Entry

    Patel AA, Gilbertson JR, Parwani AV, Dhir R, Datta MW, Gupta R, Berman JJ, Melamed J, Kajdacsy-Balla A, Orenstein J, Becich MJ; Cooperative Prostate Cancer Tissue Resource.
    An informatics model for tissue banks--lessons learned from the Cooperative Prostate Cancer Tissue Resource.
    BMC Cancer. 2006 May 5;6:120.
    PMID: 16677389
    PubMed Entry

    Berman JJ.
    Automatic extraction of candidate nomenclature terms using the doublet method.
    BMC Med Inform Decis Mak. 2005 Oct 18;5:35.
    PMID: 16232314
    PubMed Entry

    Patel AA, Kajdacsy-Balla A, Berman JJ, Bosland M, Datta MW, Dhir R, Gilbertson J, Melamed J, Orenstein J, Tai KF, Becich MJ.
    The development of common data elements for a multi-institute prostate cancer tissue bank: the Cooperative Prostate Cancer Tissue Resource (CPCTR) experience.
    BMC Cancer. 2005 Aug 21;5:108.
    PMID: 16111498
    PubMed Entry

    Berman JJ.
    Nomenclature-based data retrieval without prior annotation: facilitating biomedical data integration with fast doublet matching.
    In Silico Biol. 2005;5(3):313-22. Epub 2005 Apr 3.
    PMID: 15984939
    PubMed Entry

    Berman JJ, Bhatia K.
    Biomedical data integration: using XML to link clinical and research data sets.
    Expert Rev Mol Diagn. 2005 May;5(3):329-36. Review.
    PMID: 15934811
    PubMed Entry

    Datta MW, Dhir R, Dobbin K, Bosland MC, Melamed J, Becich MJ, Orenstein JM, Kajdacsy-Balla AA, Patel A, Macias V, Berman JJ.
    Prostate cancer in patients with screening serum prostate specific antigen values less than 4.0 ng/dl: results from the cooperative prostate cancer tissue resource.
    J Urol. 2005 May;173(5):1546-1551.
    PMID: 15821483
    PubMed Entry

    Berman JJ.
    Pathology data integration with eXtensible Markup Language.
    Hum Pathol. 2005 Feb;36(2):139-145. Review.
    PMID: 15754290
    PubMed Entry

    Berman JJ.
    Tumor taxonomy for the developmental lineage classification of neoplasms.
    BMC Cancer. 2004 Nov 30;4:88.
    PMID: 15571625
    PubMed Entry

    Datta MW, Berman JJ, Dhir R.
    Prostate cancer with low PSA levels.
    N Engl J Med. 2004 Oct 21;351(17):1802-1803.
    PMID: 15499672
    PubMed Entry

    Berman JJ.
    Doublet method for very fast autocoding.
    BMC Med Inform Decis Mak. 2004 Sep 15;4:16.
    PMID: 15369595
    PubMed Entry

    Mitchell KJ, Becich MJ, Berman JJ, Chapman WW, Gilbertson J, Gupta D, Harrison J, Legowski E, Crowley RS.
    Implementation and evaluation of a negation tagger in a pipeline-based system for information extract from pathology reports.
    Medinfo. 2004;11(Pt 1):663-7.
    PMID: 15360896
    PubMed Entry

    Seidman JD, Soslow RA, Vang R, Berman JJ, Stoler MH, Sherman ME, Oliva E, Kajdacsy-Balla A, Berman DM, Copeland LJ.
    Borderline ovarian tumors: diverse contemporary viewpoints on terminology and diagnostic criteria with illustrative images.
    Hum Pathol. 2004 Aug;35(8):918-933.
    PMID: 15297960
    PubMed Entry

    Berman JJ.
    Borderline Ovarian Tumor Workshop, Bethesda, Maryland, August 27-28, 2003.
    Hum Pathol. 2004 Aug;35(8):907-9. No abstract available.
    PMID: 15297958
    PubMed Entry

    Melamed J, Datta MW, Becich MJ, Orenstein JM, Dhir R, Silver S, Fidelia-Lambert M, Kadjacsy-Balla A, Macias V, Patel A, Walden PD, Bosland MC, Berman JJ.
    The cooperative prostate cancer tissue resource: a specimen and data resource for cancer researchers.
    Clin Cancer Res. 2004 Jul 15;10(14):4614-21.
    PMID: 15269132
    PubMed Entry

    Berman JJ.
    Resources for comparing the speed and performance of medical autocoders.
    BMC Med Inform Decis Mak. 2004 Jun 15;4:8.
    PMID: 15198804
    PubMed Entry

    Booker DL, Berman JJ.
    Dangerous abbreviations.
    Hum Pathol. 2004 May;35(5):529-31. No abstract available.
    PMID: 15138924
    PubMed Entry

    Berman JJ.
    Tumor classification: molecular analysis meets Aristotle.
    BMC Cancer. 2004 Mar 17;4:10.
    PMID: 15113444
    PubMed Entry

    Berman JJ, Datta M, Kajdacsy-Balla A, Melamed J, Orenstein J, Dobbin K, Patel A, Dhir R, Becich MJ.
    The tissue microarray data exchange specification: implementation by the Cooperative Prostate Cancer Tissue Resource.
    BMC Bioinformatics. 2004 Feb 27;5:19.
    PMID: 15040818
    PubMed Entry

    Berman JJ.
    Zero-check: a zero-knowledge protocol for reconciling patient identities across institutions.
    Arch Pathol Lab Med. 2004 Mar;128(3):344-6.
    PMID: 14987147
    PubMed Entry

    Berman JJ.
    Pathology abbreviated: a long review of short terms.
    Arch Pathol Lab Med. 2004 Mar;128(3):347-52.
    PMID: 14987146
    PubMed Entry

    Berman JJ.
    Racing to share pathology data.
    Am J Clin Pathol. 2004 Feb;121(2):169-71. Review. No abstract available.
    PMID: 14983928
    PubMed Entry

    Berman JJ, Henson DE.
    The precancers: waiting for a classification.
    Hum Pathol. 2003 Sep;34(9):833-4. Review. No abstract available.
    PMID: 14562276
    PubMed Entry

    Berman JJ, Henson DE.
    Classifying the precancers: a metadata approach.
    BMC Med Inform Decis Mak. 2003 Jun 20;3:8. Epub 2003 Jun 20.
    PMID: 12818004
    PubMed Entry

    Berman JJ.
    A tool for sharing annotated research data: the "Category 0" UMLS (Unified Medical Language System) vocabularies.
    BMC Med Inform Decis Mak. 2003 Jun 16;3:6. Epub 2003 Jun 16.
    PMID: 12809560
    PubMed Entry

    Berman JJ, Edgerton ME, Friedman BA.
    The tissue microarray data exchange specification: a community-based, open source tool for sharing tissue microarray data.
    BMC Med Inform Decis Mak. 2003 May 23;3:5. Epub 2003 May 23.
    PMID: 12769826
    PubMed Entry

    Berman JJ.
    Concept-match medical data scrubbing. How pathology text can be used in research.
    Arch Pathol Lab Med. 2003 Jun;127(6):680-6.
    PMID: 12741890
    PubMed Entry

    Berman JJ.
    Threshold protocol for the exchange of confidential medical data.
    BMC Med Res Methodol. 2002 Nov 11;2:12. Epub 2002 Nov 11.
    PMID: 12425722
    PubMed Entry

    Berman JJ.
    Confidentiality issues for medical data miners.
    Artif Intell Med. 2002 Sep-Oct;26(1-2):25-36.
    PMID: 12234715
    PubMed Entry

    Kogan SC, Ward JM, Anver MR, Berman JJ, Brayton C, Cardiff RD, Carter JS, de Coronado S, Downing JR, Fredrickson TN, Haines DC, Harris AW, Harris NL, Hiai H, Jaffe ES, MacLennan IC, Pandolfi PP, Pattengale PK, Perkins AS, Simpson RM, Tuttle MS, Wong JF, Morse HC 3rd; Hematopathology subcommittee of the Mouse Models of Human Cancers Consortium.
    Bethesda proposals for classification of nonlymphoid hematopoietic neoplasms in mice.
    Blood. 2002 Jul 1;100(1):238-45.
    PMID: 12070033
    PubMed Entry

    Hutchins GM, Berman JJ, Moore GW, Hanzlick R.
    Practice guidelines for autopsy pathology: autopsy reporting. Autopsy Committee of the College of American Pathologists.
    Arch Pathol Lab Med. 1999 Nov;123(11):1085-92.
    PMID: 10539932
    PubMed Entry

    Berman JJ, Moore GW, Hutchins GM.
    U.S. Senate Bill 422: the Genetic Confidentiality and Nondiscrimination Act of 1997.
    Diagn Mol Pathol. 1998 Aug;7(4):192-6. Review.
    PMID: 9917128
    PubMed Entry

    Berman JJ, Moore GW, Hutchins GM. Internet autopsy database.
    Hum Pathol. 1997 Apr;28(4):393-4. No abstract available.
    PMID: 9104935
    PubMed Entry

    Berman JJ, Moore GW.
    SNOMED-encoded surgical pathology databases: a tool for epidemiologic investigation.
    Mod Pathol. 1996 Sep;9(9):944-50.
    PMID: 8878028
    PubMed Entry

    Moore GW, Berman JJ, Hanzlick RL, Buchino JJ, Hutchins GM.
    A prototype Internet autopsy database. 1625 consecutive fetal and neonatal autopsy facesheets spanning 20 years.
    Arch Pathol Lab Med. 1996 Aug;120(8):782-5.
    PMID: 8718907
    PubMed Entry

    Berman JJ, Moore GW, Hutchins GM.
    Maintaining patient confidentiality in the public domain Internet Autopsy Database (IAD).
    Proc AMIA Annu Fall Symp. 1996;:328-32.
    PMID: 8947682
    PubMed Entry

    Berman JJ, Borkowski A, Rachocka H, Moore GW.
    Impact of unfunded research in medicine, pathology, and surgery.
    South Med J. 1995 Mar;88(3):295-9.
    PMID: 7886525
    PubMed Entry

    Berman JJ, Moore GW.
    Image analysis software for the detection of preneoplastic and early neoplastic lesions.
    Cancer Lett. 1994 Mar 15;77(2-3):103-9.
    PMID: 8168056
    PubMed Entry

    Moore GW, Berman JJ.
    Performance analysis of manual and automated systemized nomenclature of medicine (SNOMED) coding.
    Am J Clin Pathol. 1994 Mar;101(3):253-6.
    PMID: 8135178
    PubMed Entry

    Berman JJ, Moore GW, Alonsozana EL, Mamo GF.
    Elevated prostate-specific antigen level and the negative prostate biopsy.
    South Med J. 1994 Feb;87(2):290-1. No abstract available.
    PMID: 7509507
    PubMed Entry

    Moore GW, Berman JJ.
    Automatic SNOMED coding.
    Proc Annu Symp Comput Appl Med Care. 1994;:225-9.
    PMID: 7949924
    PubMed Entry

    Berman JJ, Moore GW, Donnelly WH, Massey JK, Craig B.
    A SNOMED analysis of three years' accessioned cases (40,124) of a surgical pathology department: implications for pathology-based demographic studies.
    Proc Annu Symp Comput Appl Med Care. 1994;:188-92.
    PMID: 7949917
    PubMed Entry

    Berman JJ, Moore GW.
    The role of cell death in the growth of preneoplastic lesions: a Monte Carlo simulation model.
    Cell Prolif. 1992 Nov;25(6):549-557.
    PMID: 1457604
    PubMed Entry

    Berman JJ, Moore GW.
    Spontaneous regression of residual tumour burden: prediction by Monte Carlo simulation.
    nal Cell Pathol. 1992 Sep;4(5):359-68.
    PMID: 1445794
    PubMed Entry Borkowski A, Berman JJ, Moore GW. Research by pathologists not funded by external grant agencies: a success story. Mod Pathol. 1992 Sep;5(5):577-579. PMID: 1344824
    PubMed Entry

    Moore GW, Brown LA, Miller RE.
    Goedelization of a Pathology Database: Re-identification by Inference. (Abstract).
    Arch Pathol Lab Med. 2002 Jun;126:8xx.
    http://www.netautopsy.org/apep01go.htm

    Giere W, Moore GW.
    Goethe University Autopsy Register: Anonymized Bilingual Database. (Abstract).
    Arch Pathol Lab Med. 2002 Jun;126:8xx.
    http://www.netautopsy.org/apep01gu.htm

    Moore GW, Brown LA, Miller RE.
    Set Theory Definition and Algorithm for Medical De-Identification. (Abstract).
    Arch Pathol Lab Med. 2001 Jun;125:8xx.
    http://www.netautopsy.org/apep01st.htm

    Miller RE, Boitnott JK, Moore GW.
    Web-based Free-Text Query System for Surgical Pathology Reports with Automatic Case De-Identification. (Abstract).
    Arch Pathol Lab Med. 2001 Jun;125:8xx.
    http://www.netautopsy.org/apep01wb.htm

    Alonsozana GLG, Moore GW, Hutchins GM.
    UMLS Concordance for Human Embryology. (Abstract).
    Arch Pathol Lab Med. 2001 Jun;125:8xx.

    Sinard JH, Moore GW.
    UMLS Concordance for a Comprehensive Pathology Text.
    Arch Pathol Lab Med. 2001 Jun;125:8xx.

    Moore GW, Miller RE.
    Linguistic Inventory of the Johns Hopkins Surgical Pathology Database. (Abstract).
    Arch Pathol Lab Med. 2001 Jun;125:8xx.
    http://www.netautopsy.org/vhspapsx.htm

    Moore GW, Brenner DS, Berman JJ.
    Automatic Indexing of a Pathology Image Archive using UMLS. (Abstract).
    Arch Pathol Lab Med. 2000 Jun;124:809.
    http://www.netautopsy.org/apep99im.htm

    Kao GF, Moore GW.
    Dermatopathology False Negative Terms in UMLS. (Abstract).
    Arch Pathol Lab Med. 2000 Jun;124:809.
    http://www.netautopsy.org/apep99dr.htm

    Nonaka D, Moore GW, Satomura Y.
    Japanese Language Annotation of an Internet Pathology Image Archive. (Abstract).
    Arch Pathol Lab Med. 2000 Jun;124:820.
    http://www.netautopsy.org/apep99jp.htm

    Moore GW, Vardar E, Erozan YS, Durmusoglu F.
    Turkish Language Annotation of an Internet Pathology Image Archive. (Abstract).
    Arch Pathol Lab Med. 2000 Jun;124:820.
    http://www.netautopsy.org/apep99tk.htm

    Moore GW, Brown LA, Miller RE.
    Gödelization of a Pathology Database. Re-identification by Inference. (Abstract).
    Arch Pathol Lab Med. 2002 Jun;126:.
    http://www.netautopsy.org/apep01go.htm

    Giere W, Moore GW, Hutchins GM.
    Goethe University Autopsy Register: Anonymous Bilingual Database. (Abstract).
    Arch Pathol Lab Med. 2002 Jun;126:.
    http://www.netautopsy.org/apep01gu.htm

    Moore GW, Brown LA, Burger RH, Hutchins GM, Miller RE.
    Modal Logic Theory for Pathology Inference. (Abstract).
    Arch Pathol Lab Med. 2004 Jun;128:.
    http://www.netautopsy.org/modlthry.htm

    Moore GW, Brown LA, Burger RH, Kao GF, Hutchins GM, Miller RE.
    Spreadsheet Order Logic for Pathology Inference. (Abstract).
    Arch Pathol Lab Med. 2005 Jun;129:.
    http://www.netautopsy.org/ordrlogc.htm

    Moore GW, Struble RA, Brown LA, Kao GF, Hutchins GM.
    Infinite Papilloma: Model for Unbounded Tumor Growth. (Abstract).
    Arch Pathol Lab Med. 2006 Jun;130:898.
    http://www.netautopsy.org/infnpapl.htm

    Moore GW, Struble RA, Brown LA, Kao GF, Hutchins GM.
    Cell Surface Tessellation: Model for Malignant Growth. (Abstract).
    Arch Pathol Lab Med. 2007 Jun;131:.
    http://www.netautopsy.org/celltess.htm


    Last revised: 1/3/2007, G. William Moore, MD, PhD.