[Representation of knowledge in respiratory medicine: ontology should help the coding process]

INTRODUCTION

Access to medical knowledge is a major issue for health professionals and requires the development of terminologies. The objective of the reported work was to construct an ontology of respiratory medicine, i.e. an organized and formalized terminology composed by specific knowledge. The purpose is to help the medico-economical coding process and to represent the relevant knowledge about the patient.

METHODS

Our researches cover the whole life cycle of an ontology, from the development of a methodology, to building it from texts, to its use in an operational system. A computerized tool, based on the ontology, allows both a medico-economical coding and a graphical medical one. This second one will be used to index hospital reports.

RESULTS

Our ontology counts 1913 concepts and contains all the knowledge included in the PMSI part of the SPLF thesaurus. Our tool has been evaluated and showed a recall of 80% and an accuracy of 85% regarding the medico-economical coding.

CONCLUSION

The work presented in this paper justifies the approach that has been used. It must be continued on a large scale to validate our coding principles and the possibility of making enquiries on patient reports concerning clinical research.

Building an ontology of pulmonary diseases with natural language processing tools using textual corpora

Pathologies and acts are classified in thesauri to help physicians to code their activity. In practice, the use of thesauri is not sufficient to reduce variability in coding and thesauri are not suitable for computer processing. We think the automation of the coding task requires a conceptual modeling of medical items: an ontology. Our task is to help lung specialists code acts and diagnoses with software that represents medical knowledge of this concerned specialty by an ontology. The objective of the reported work was to build an ontology of pulmonary diseases dedicated to the coding process. To carry out this objective, we develop a precise methodological process for the knowledge engineer in order to build various types of medical ontologies. This process is based on the need to express precisely in natural language the meaning of each concept using differential semantics principles. A differential ontology is a hierarchy of concepts and relationships organized according to their similarities and differences. Our main research hypothesis is to apply natural language processing tools to corpora to develop the resources needed to build the ontology. We consider two corpora, one composed of patient discharge summaries and the other being a teaching book. We propose to combine two approaches to enrich the ontology building: (i) a method which consists of building terminological resources through distributional analysis and (ii) a method based on the observation of corpus sequences in order to reveal semantic relationships. Our ontology currently includes 1550 concepts and the software implementing the coding process is still under development. Results show that the proposed approach is operational and indicates that the combination of these methods and the comparison of the resulting terminological structures give interesting clues to a knowledge engineer for the building of an ontology.

Methodology to build medical ontology from textual resources

In the medical field, it is now established that the maintenance of unambiguous thesauri goes through ontologies. Our research task is to help pneumologists code acts and diagnoses with a software that represents medical knowledge through a domain ontology. In this paper, we describe our general methodology aimed at knowledge engineers in order to build various types of medical ontologies based on terminology extraction from texts. The hypothesis is to apply natural language processing tools to textual patient discharge summaries to develop the resources needed to build an ontology in pneumology. Results indicate that the joint use of distributional analysis and lexico-syntactic patterns performed satisfactorily for building such ontologies.

Building medical ontologies based on terminology extraction from texts: an experimentation in pneumology

Pathologies and acts are classified in thesauri to help physicians to code their activity. In practice, the use of thesauri is not sufficient to reduce variability in coding and thesauri do not fit computer processing. We think the automation of the coding task requires a conceptual modelling of medical items: an ontology. Our objective is to help pneumologists code acts and diagnoses with a software that represents medical knowledge by an ontology of the concerned specialty. The main research hypothesis is to apply natural language processing tools to corpora to develop the resources needed to build the ontology. In this paper, our objective is twofold: we have to build the ontology of pneumology and we want to develop a methodology for the knowledge engineer to build various types of medical ontologies based on terminology extraction from texts.