Analysis and Design of an Ontology for Intensive Care Diagnoses

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.

[1]  R. Fetter,et al.  Case mix definition by diagnosis-related groups. , 1980, Medical care.

[2]  J S Gonnella,et al.  Staging of disease. A case-mix measurement. , 1984, JAMA.

[3]  W. Knaus,et al.  APACHE II: a severity of disease classification system. , 1985 .

[4]  Relman As,et al.  Assessment and accountability: the third revolution in medical care. , 1988 .

[5]  William E. Lorensen,et al.  Object-Oriented Modeling and Design , 1991, TOOLS.

[6]  W. Knaus,et al.  The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. , 1991, Chest.

[7]  M A Musen,et al.  Representation of clinical data using SNOMED III and conceptual graphs. , 1992, Proceedings. Symposium on Computer Applications in Medical Care.

[8]  S. Lemeshow,et al.  Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients. , 1993, JAMA.

[9]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993, Knowl. Acquis..

[10]  S. Lemeshow,et al.  A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study , 1993 .

[11]  D. Lindberg,et al.  The Unified Medical Language System , 1993, Methods of Information in Medicine.

[12]  Ameen Abu-Hanna,et al.  Modeling domain knowledge using explicit conceptualization , 1994, IEEE Expert.

[13]  Mark A. Musen,et al.  Research Paper: A Logical Foundation for Representation of Clinical Data , 1994, J. Am. Medical Informatics Assoc..

[14]  Susan J. Grobe.  Informatics: The Infrastructure for Quality Assessment and Quality Improvement , 1995, J. Am. Medical Informatics Assoc..

[15]  Yuval Shahar,et al.  Ontology-based configuration of problem-solving methods and generation of knowledge-acquisition tools: application of PROTEGE-II to protocol-based decision support , 1995, Artif. Intell. Medicine.

[16]  Thomas R. Gruber,et al.  Toward principles for the design of ontologies used for knowledge sharing? , 1995, Int. J. Hum. Comput. Stud..

[17]  Guus Schreiber,et al.  A case study in ontology library construction , 1995, Artif. Intell. Medicine.

[18]  Rita D. Zielstorff,et al.  Review: Capturing and Using Clinical Outcome Data: Implications for Information Systems Design , 1995, J. Am. Medical Informatics Assoc..

[19]  D J Rothwell.  SNOMED-based knowledge representation. , 1995, Methods of information in medicine.

[20]  Alan L. Rector,et al.  Research Paper: Medical-Concept Models and Medical Records: An Approach Based on GALENand PEN&PAD , 1995, J. Am. Medical Informatics Assoc..

[21]  A. Rector,et al.  A Terminology Server for Medical Language and Medical Information Systems , 1995, Methods of Information in Medicine.

[22]  D J Rothwell,et al.  Managing information with SNOMED: understanding the model. , 1996, Proceedings : a conference of the American Medical Informatics Association. AMIA Fall Symposium.

[23]  Roel Wieringa,et al.  Requirements Engineering: Frameworks for Understanding , 1996 .

[24]  Ian Horrocks,et al.  The GRAIL concept modelling language for medical terminology , 1997, Artif. Intell. Medicine.

[25]  L. Nelson.  Data, data everywhere. , 1997, Critical care medicine.

[26]  S. Keenan,et al.  Assessing the efficiency of the admission process to a critical care unit: does the literature allow the use of benchmarking? , 1997, Intensive Care Medicine.

[27]  Milholland Dk.  Information systems in critical care: a measure of their effectiveness. , 1998 .

[28]  Ivar Jacobson,et al.  The Unified Modeling Language User Guide , 1998, J. Database Manag..