Download PDF
Methods Inf Med 1999; 38(02): 102-112
DOI: 10.1055/s-0038-1634178
Original Article
Schattauer GmbH

Analysis and Design of an Ontology for Intensive Care Diagnoses

N. F. de Keizer
1   Department of Medical Informatics, Academic Medical Center, Amsterdam, The Netherlands
3   In cooperation with the NICE foundation, Department of Intensive Care, Academic Medical Center, Amsterdam, The Netherlands
,
A. Abu-Hanna
1   Department of Medical Informatics, Academic Medical Center, Amsterdam, The Netherlands
,
R. Cornet
1   Department of Medical Informatics, Academic Medical Center, Amsterdam, The Netherlands
,
J. H. M. Zwetsloot-Schonk
2   Julius Center for Patient Oriented Research, Utrecht University Medical School Utrecht, The Netherlands
,
C. P. Stoutenbeek
3   In cooperation with the NICE foundation, Department of Intensive Care, Academic Medical Center, Amsterdam, The Netherlands
› Author Affiliations
Further Information

Publication History

Publication Date:
08 February 2018 (online)

    Permissions and Reprints

Abstract

Information about the patient‘s health status and about medical problems in general, play an important role in stratifying a patient population for quality assurance of intensive care. A terminological system which supports both the description of health problems for daily care practice and the aggregation of diagnostic information for evaluative research, is desirable for description of the patient population. This study describes the engineering of an ontology that facilitates a terminological system for intensive care diagnoses. We analyzed the criteria for such an ontology and evaluated existing terminological systems according to these criteria. The analysis shows that none of the existing terminological systems completely satisfies all our criteria. We describe choices regarding design, content and representation of a new ontology on which an adequate terminological system is based. The proposed ontology is characterized by the explicit and formal representation of the domain model, the metaspecification of its concepts, the vocabulary to define concepts and the nomenclature to support the composition of new concepts.

Keywords

Ontology - Conceptual Modeling - Terminological System - Nomenclature - Intensive Care - Diagnosis
 

  • References

  • 1 Keenan S, Doig G, Martin C, Inman KWJS. Assessing the efficiency of the admission process to a critical care unit: does the literature allow the use of benchmarking. Intensive Care Med 1997; 23: 574-80.
    CrossrefPubMedGoogle Scholar
  • 2 Relman A. Assessment and accountability. The third revolution in medical care. N Engl J Med 1988; 319: 1220-2.
    CrossrefPubMedGoogle Scholar
  • 3 ICNARC Case Mix Programme Dataset Specifications. London: ICNARC; 1996
    Google Scholar
  • 4 Nelson L. Data, data everywhere (editorial comment). Crit Care Med 1997; 25 (Suppl. 08) 1265.
    CrossrefPubMedGoogle Scholar
  • 5 Apolone G, Bertolini G, D’Amico R. et al. The performance of the SAPS II on a cohort of patients admitted to 99 Italian ICUs: results from GiViTi. Gruppo Italiano per la Valutazione degli interventi in Terapia Intensiva. Int Care Med 1996; 22 (Suppl. 12) 1368-78.
    CrossrefPubMedGoogle Scholar
  • 6 DeKeizer N, Bouman R, Joode J. Anatomical Quality Assurance System (in Dutch). Medisch Contact 1999; 54: 276-9.
    PubMedGoogle Scholar
  • 7 Knaus W, Draper E, Wagner D, Zimmerman J. APACHE II: A severity of disease classification. Crit Care Med 1985; 10: 818-29.
    PubMedGoogle Scholar
  • 8 Knaus W, Wagner D, Draper E, Zimmerman J, Bergner M, Bastos P. The APACHE III Prognostic System. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest 1991; 100: 1619-36.
    CrossrefPubMedGoogle Scholar
  • 9 Le Gall J, Lemeshow S, Saulnier F. A new simplified acute physiology score (SAPS II) based on a European/North American multicenter study. JAMA 1993; 24: 2957-63.
    PubMedGoogle Scholar
  • 10 Lemeshow S, Teres D, Klar J, Avrunin J, Gehlbach S, Rapoport J. Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients. JAMA 1993; 20: 2478-86.
    PubMedGoogle Scholar
  • 11 Gonella J, Hornbrook M, Louis D. Staging of diseases: A case mix measurement. JAMA 1984; 251 (Suppl. 05) 637-44.
    CrossrefPubMedGoogle Scholar
  • 12 Fetter R, Skin Y, Freeman J, Averill R, Thompson J. Case Mix Definition by DRG. Medical Care. 1980: XVIII.
    PubMedGoogle Scholar
  • 13 Milholland D. Information systems in critical care: a measure of their effectiveness. In: Greenes R, Peterson H, Protti D. eds. Medinfo. Healthcare Computing & Communication. Canada: 1995: 1068-70.
    Google Scholar
  • 14 Grobe J. Editorial comments: Informatics: The infrastructure for quality assessment and quality improvement. J AM Med Informatics Assoc 1995; 2: 267-8.
    CrossrefPubMedGoogle Scholar
  • 15 Zielstorff R. Capturing and using clinical outcome data: implications for information systems design. J AM Med Informatics Assoc 1995; 2: 191-6.
    CrossrefPubMedGoogle Scholar
  • 16 Gruber T. Towards principles for the design of ontologies used for knowledge sharing. Intern J Human-Comput Stud 1995; 43: 907-28.
    PubMedGoogle Scholar
  • 17 European prestandard.. Medical informatics – Categorical structures of systems of concepts – Model for representation of semantics. Brussel: CEN; 1997
    Google Scholar
  • 18 de Keizer N, Stoutenbeek C, Hanneman L, de Jonge E. A PDMS evaluation in Dutch intensive care. Intensive Care Med 1998; 24: 167-71.
    CrossrefPubMedGoogle Scholar
  • 19 Cimino J, Clayton P, Hripcsak G, Johnson S. Knowledge-based approaches to the maintenance of a large controlled medical terminology. J Am Med Informatics Assoc 1994; 1: 35-50.
    CrossrefPubMedGoogle Scholar
  • 20 Campbell J, Carpenter P, Sneiderman C. et al. Phase II evaluation of clinical coding schemes: completeness, definitions and clarity. J Am Med Informatics Assoc 1997; 4: 238-51.
    CrossrefPubMedGoogle Scholar
  • 21 International Classification of Diseases, manual of the International Statistical Classification of diseases, injuries and causes of death: 9th revision. WHO. 1977
    PubMedGoogle Scholar
  • 22 International Classification of Diseases, manual of the International Statistical Classification of diseases, injuries and causes of death: 10th revision. WHO. 1993
    PubMedGoogle Scholar
  • 23 Read J, Sanderson H, Drennan Y. Terming, coding and grouping. In: RAG, ed. Medinfo 95. 1995: 56-9.
    Google Scholar
  • 24 Rothwell D. SNOMED-Based knowledge representation. Meth Inform Med 1995; 34: 209-13.
    PubMedGoogle Scholar
  • 25 Lindberg D, Humphreys B, Mc Cray A. The Unified Medical Language System. Meth Inform Med 1993; 34: 281-91.
    PubMedGoogle Scholar
  • 26 Rector A, Solomon W, Nowlan W, Rush T, Zanstra P, Claassen W. A Terminology Server for medical language and medical information systems. Meth Inform Med 1995; 34: 147-57.
    PubMedGoogle Scholar
  • 27 Rector A, Glowinski A, Nowlan W, Rossi-Mori A. Medical-concept models and medical records: an approach based on GALEN and PEN & PAD. J Am Med Inform Assoc 1995; 2: 19-35.
    CrossrefPubMedGoogle Scholar
  • 28 Rector A, Bechhofer S, Goble C, Horrocks I, Nowlan W, Solomon W. The Grail concept modelling language for medical terminology. Artif Intell 1997; 9: 139-71.
    CrossrefPubMedGoogle Scholar
  • 29 Chen P. The Entity-Relationship model; toward a unified view of data. ACM Trans on Database Systems; 1966
    Google Scholar
  • 30 Campbell K, Das A, Musen M. A logical foundation for representation of clinical data. J Am Med Informatics Assoc 1994; 1: 218-32.
    CrossrefPubMedGoogle Scholar
  • 31 Rumbaugh J. et al. Object-Oriented Modeling and Design. Prentice-Hall; 1991
    Google Scholar
  • 32 Booch G, Rumbaugh J, Jacobson I. Unified Modeling Language User Guide. Addison-Wesley; 1998
    Google Scholar
  • 33 Sowa J. Conceptual structures. Addison-Wesley; 1984. Reading M. ed.
    Google Scholar
  • 34 Gruber T. A translation approach to portable ontology specifications. Knowledge Acquisition 1993; 5: 199-220.
    CrossrefPubMedGoogle Scholar
  • 35 Dutch Classification and Terminology Committee for Health.. Handbook Standardisation of Classification and Definitions in Health Care. Zoetermeer: WCC; 1990
    Google Scholar
  • 36 Abu-Hanna A, Jansweijer W. Modeling application domain knowledge using explicit conceptualization. IEEE-Expert; 1994
    Google Scholar
  • 37 Heijst G, Falasconi S, Abu-Hanna A, Schreiber A, Stefanelli M. A case study in ontology library construction. Art Intell, Med 1995; 7: 227-55.
    CrossrefPubMedGoogle Scholar
  • 38 Tu S, Eriksson H, Gennari J, Shahar Y, Musen M. Ontology-based configuration of problem-solving methods and generation of knowledge acquisition tools: the application of PROTEGE-II to protocol-based desicion support. Art Intell Med 1995; 7: 257-90.
    CrossrefPubMedGoogle Scholar
  • 39 Campbell K, Musen M. Representation of Clinical Data Using SNOMED III and conceptual graphs. In: MEF, ed. SCAMC: McGraw Hill; 1992: 354-8.
    Google Scholar
  • 40 Rothwell D, Coté R. Managing Information with SNOMED: Understanding the model. SCAMC; 1996: 80-3.
    Google Scholar
  • 41 Wieringa R. Requirements engineering. Framework for understanding. John Wiley & Sons; 1996
    Google Scholar