Formalized Terminologies to support tasks at Intensive Care Units of Hospitals (DICE/I-Catcher).
By: Michel Klein and Ronald Cornet. June 2007.
General Description
Clinical trials use entry criteria to select patients for the study. The choice of these criteria is an important step in clinical trial design. To be able to compare the results of the trial with those of other trials and to assess the generalizability of the results to daily clinical practice, the entry criteria have to be compatible with definitions used in comparable trials and the agreed standard definitions of disease. Description Logics, the logical foundations for ontology modeling are obvious candidates to model these entry criteria, to allow for declarative descriptions of classes of patients with particular symptoms. This is obviously complicated when no crisp disease definition exists. For this purpose we model clinical trials in an extension of Dls, so called Rough Description Logics, and use the semantics of these languages to study 9 different clinical trials about the sepsis condition.
The problem
Our current research was motivated by a recent study of the definitions for sepsis used in clinical trials. Before a medical treatment can be used in daily clinical practice, its effect and impact on the patient have to be investigated in a clinical trial. When several trials have been performed it is interesting to compare the results of those trials. Unfortunately, the nine different trials that were investigated in [Peelen et al. , 2005] showed too much variation in their definitions of severe sepsis patients to enable a fair comparison of trial results. Illustration 1 shows the mortality rate for these nine trials. Obviously, a proper comparison of the outcomes of different trials is almost impossible if the patient population is incomparable.
One of the reasons for the problems of defining valid trials in this domain is that there is no accepted general definition of the sepsis condition. Medicine is a typical domain where concepts cannot always be described in a crisp manner. E.g., the definition of a disease is not always clear-cut, especially if a single marker is lacking that distinguishes a patient with a disease from a patient without the disease. This is common in psychiatry and in diseases in which the underlying pathology of the disease is unclear. An example of the latter is sepsis.
To compare the patient data of one hospital with the data of another, for example for quality assessment purposes, the controlled vocabulary in one hospital has to be expressed in terms of the DICE ontology. To provide a basis for this mapping ontology alignment techniques can be used. When the controlled vocabulary is mapped in this way, the DICE ontology can be used to query the data of the other hospital.
Key Benefits of Using Semantic Web Technology
Key benefits of Semantic Web technology for patient registration at Intensive Care Units include:
• The use of formal ontologies allow for dynamic selection of patient groups, which in turn facilitates daily care practice and management tasks.
• Ontology alignment techniques developed in the context of Semantic Web allow to combine and compare patient registrations from different hospitals.
• The reasoning support that is available for OWL DL ontologies help to verify the terminology system.
See Other Use Cases
