Related Experiment Video
Updated: Jul 13, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
[Classification in medicine. An introductory reflection on its aim and object]
1J.W. Goethe-Universität, Taunusstein, BRD. W.Giere@t-online.de
Abstract:
Human beings are born with the ability to recognize Gestalt and to classify. However, all classifications depend on their circumstances and intentions. There is no ultimate classification, and there is no one correct classification in medicine either. Examples for classifications of diagnoses, symptoms and procedures are discussed. The path to gaining knowledge and the basic difference between collecting data (patient file) and sorting data (register) will be illustrated using the BAIK information model. Additionally the model shows how the doctor can profit from the active electronic patient file which automatically offers him other relevant information for his current decision and saves time. "Without classification no new knowledge, no new knowledge through classification". This paradox will be solved eventually: a change of paradigms requires the overcoming of the currently valid classification system in medicine as well. Finally more precise recommendations will be given on how doctors can be freed from the burden of the need to classify and how the whole health system can gain much more valid data without limiting the doctors' freedom and creativity through co-ordinated use of IT, all while saving money at the same time.
Related Concept Videos
Classification of Illness
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...
Drug Classes and Categories
Cardiovascular Drugs: Classification based on Therapeutic Indications
Methods of Classification and Identification
Microbial Classification System
Introduction to Language of Pathophysiology ll
