WHAT IS A
MEDICAL ONTOLOGY?
DRAFT COPY ONLY.
http://www.medparse.com/whatonto.htm

G. William Moore, MD, PhD [1,2,3],

From: Pathology and Laboratory Medicine Service (113), Baltimore VA Maryland Health Care System [1], Baltimore, MD.
Department of Pathology, University of Maryland School of Medicine [2], Baltimore, MD.
Department of Pathology, The Johns Hopkins Medical Institutions [3], Baltimore, MD.


1. MEDICAL ONTOLOGIES.



      1. An ONTOLOGY is a (Platonic) description of essential reality, i.e., what actually is, as opposed to what one can see (observation, accident), or what one can know (epistemiology) (12). The term ontology was coined by two German philosophers, Göckel and Lorhard, in 1613, and first appeared in English in 1721. Quine (13) views ontology as the metaphysical commitments or presuppositions embodied in the different natural sciences. For example, the belief that a cancer can metastasize would be an ONTOLOGICAL COMMITMENT. In the philosophy and practice of science, ontology goes under various names: essence, reality, Mind of God, nature, gold standard, or mathiverse (14). In medical informatics, ontology has come to mean a structured list of concepts, typically prepared by an expert or panel of experts.

      2. With the ease of posting structured lists on the Internet, and with EXTENDED MARKUP LANGUAGE (XML) (38,39) as an emerging standard for such lists, it is likely that the next decade will witness an explosion of public medical ontologies, both amateur and professional.

      3. The importance of ontologies has been recognized by the U. S. Defense Advanced Research Projects Agency (DARPA), the original sponsor of the Internet, which has proposed guidelines for a formal ontology AGENT MARKUP LANGUAGE, that employs the ONTOLOGY INFERENCE LAYER (15,16).

      4. A simple ontology is illustrated by the observation at autopsy that CHRONIC-PASSIVE-CONGESTION-LIVER (CPCL = C0700148-C0721399 in UMLS codes) (40,41). often accompanies HEART HYPERTROPHY (HH = C0795691-C0333959). In an approximate sense, HH causes CPCL (42). Thus, one might expect a hypothetical collection, say, of 10,000 cases to distribute as follows: 
              HEART HYPERTROPHY (HH=C0795691-C0333959)
 ____________________________________________
|            |  NOT-HH |     HH   |  Total  |
|___________________________________________|
|  NOT-CPCL  |   7,000 |    1,000 |  8,000  |
|___________________________________________|
|    CPCL    |       0 |    2,000 |  2,000  |
|___________________________________________|
|   Total    |   7,000 |    3,000 | 10,000  |
|___________________________________________|
 CHRONIC-PASSIVE
 -CONGESTION-LIVER
 (CPCL=C0700148-C0721399).


      5. That is, most cases are negative for both features; HH anticipates CPCL in some cases; but there should be only rare cases with CPCL but without HH. Therefore, in the language of first-order propositional logic, CPCL IMPLIES HH, or NOT-CPCL IOR HH.

      6. Such correlations (2x2 CONTINGENCY TABLES), could be edited for redundancy and nonsense correlations (43).

      7. As necessary, a collection of such 2x2 contingency tables could be ORDERED BY IMPORTANCE, based upon the frequencies of cases appearing in the lower right corner of the table.

      8. Medical ontologies can be used to break into de-identified, public databases BY INFERENCE, which are otherwise well-protected. It will become the obligation of the data-holder to anticipate any inferential break-ins, and pre-empt them, by removing vulnerable data (5,44).


2. REFERENCES.



      1. U. S. Code of Federal Regulations. 1995. 45 CFR Subtitle A (10-1-95 Edition), part 46.101 (b) (4).
U. S. Department of Health and Human Services. Office of the Secretary.
The complete Common Rule document (45CFR46), at URL:
http://www.uaf.edu/oar/irb/45cfr46.html
or at URL:
http://ohrp.osophs.dhhs.gov/humansubjects/guidance/45cfr46.htm

      2. U. S. Code of Federal Regulations. 1999. 45 CFR Parts 160 - 164. Standards for Privacy of Individually Identifiable Health Information; Proposed Rule.
Department of Health and Human Services. Office of the Secretary.
Fed Regist. 1999 Nov 3;64(212):59917-59966. http://aspe.hhs.gov/admnsimp/

      3. National Cancer Institute's Confidentiality Brochure, at URL:
http://www-cdp.ims.nci.nih.gov/policy.html

      4. Moore GW, Berman JJ.
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Copyright Springer-Verlag: Berlin/Heidelberg 1999.

      5. Sweeney L.
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PhD Thesis. Massachusetts Institute of Technology. Spring, 2001. Draft.
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      6. Sweeney L.
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      7. Sweeney L.
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      10. Hofstadter DR.
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      11. Casti JL, DePauli W.
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      14. Stewart I.
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      15. U. S. Defense Advanced Research Projects Agency (DARPA). Agent Markup Language.
http://www.daml.org

      16. U. S. Defense Advanced Research Projects Agency (DARPA). Ontology Inference Layer.
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      17. Boole G.
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      22. Nguyen HT, Walker EA.
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      24. Beeson P.
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      25. Sutton WF, with Linn E.
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      26. Moore GW, Hutchins GM, Bulkley BH.
Certainty levels in the nullity method of symbolic logic: application to the pathogenesis of congenital heart malformations.
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      27. Moore GW, Hutchins GM.
Effort and demand logic in medical decision making.
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      28. Cios KJ, Moore GW. 2000.
Medical Data Mining and Knowledge Discovery: An Overview.
In: Cios KJ, ed. Medical Data Mining and Knowledge Discovery.
2001. XVIII, 502 pp. 98 figs., 98 tabs. Hardcover.
ISBN: 3-7908-1340-0.
Copyright Springer-Verlag: Berlin/Heidelberg 1999.

      29. College of American Pathologists.
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      30. Collaborative Prostate Cancer Tissue Resource.
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      31. Suppes P.
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      32. Bernays P.
Axiomatic Set Theory.
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      33. Suppes P.
Axiomatic Set Theory.
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      34. Davis M.
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      35. Andrews GL.
Number Theory.
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      36. Schneier B.
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      37. Tarjan RE.
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      38. Worldwide Web Consortium.
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      39. Light R.
Presenting XML.
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      40. U.S. National Library of Medicine.
Unified Medical Language System.
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      41. U. S. National Library of Medicine.
UMLS Knowledge Sources. Twelfth Edition. Unified Medical Language System.
U. S. Department of Health and Human Services. National Institutes of Health. National Library of Medicine. 2001.

      42. Vigorita VJ, Moore GW, Hutchins GM.
Absence of correlation between coronary arterial atherosclerosis and severity or duration of diabetes mellitus of adult onset.
Am J Cardiol. 1980;46:535-542.

      43. Moore GW, Hutchins GM.
Consistency versus completeness in medical decision making: Application to 155 patients autopsied after coronary artery bypass graft surgery.
Proc 6th Annu Symp Comput Appl Med Care. 1982;6:805-811.

      44. Moore GW, Brown LA, Miller RE.
Set Theory Definition and Algorithm for Medical De-Identification.
Arch Pathol Lab Med. 2001;:in press.
http://www.netautopsy.org/apep00st.htm

      45. Johns Hopkins Autopsy Resource.
http://www.netautopsy.org/

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

      47. Hornung J.
Kritik der Signifikanztests.
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      48. Moore GW, Hutchins GM.
The persistent importance of autopsies.
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      49. Wilbur WJ.
Overview of Books at NCBI.
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      50. Shared Pathology Informatics Network.
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      51. Nelson SJ, Cole WG, Tuttle MS, Olson NE, Sherertz DD.
Recognizing new medical knowledge computationally.
Proc Annu Symp Comput Appl Med Care. 1993;17:409-413.

      52. Moore GW, Polacsek RA, Erozan YS, de la Monte SM, Miller RE, Hutchins GM, Riede UN.
Multilingual translation techniques in the analysis of narrative medical text.
Comput Methods Programs Biomed. 1986 Mar;22(1):35-42.

      53. Chomsky N.
Aspects of the Theory of Syntax.
Cambridge, MA: The MIT Press. 1965.


Last Updated: September 16, 2001, by G. William Moore, MD, PhD.