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 by terminology extraction from texts: an experiment for the intensive care units.

In many medical fields, maintenance, comparison and aggregation of unambiguous terminologies go through formal specialized clinical terminologies: ontologies. We describe a methodology to build medical ontology from textual reports using a natural language processing tool, the SYNTEX software. The methodology is illustrated in the surgical intensive care medical domain. We have tested the possibility for an expert to build a sizeable ontology in a reasonable time. The quality of the ontology has been evaluated according to its capacity to cover the ICD-10 terminology in the field. Finally, the methodology itself is discussed

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

Towards an automatic harmonization of the representation of medical reports to assess their similarities.

Numerous hospitals contain unexploited knowledge deposits. These often take the form of unstructured records with heterogeneous content, which, at various levels of those organizations, register past cases. Those records are for instance patient medical records. Accessing the knowledge and experience they gather would help us to handle present cases. We present here a method to normalize textual reports in foetopathology in order to constitute a proper case base that will be the target of case-based reasoning techniques. Statistics of noise and silence generated by this method on 10 cases are presented

Semantic interoperability challenges to process large amount of data perspectives in forensic and legal medicine.

This article is a position paper dealing with semantic interoperability challenges. It addresses the Variety and Veracity dimensions when integrating, sharing and reusing large amount of heterogeneous data for data analysis and decision making applications in the healthcare domain. Many issues are raised by the necessity to conform Big Data to interoperability standards. We discuss how semantics can contribute to the improvement of information sharing and address the problem of data mediation with domain ontologies. We then introduce the main steps for building domain ontologies as they could be implemented in the context of Forensic and Legal medicine. We conclude with a particular emphasis on the current limitations in standardisation and the importance of knowledge formalization

From Patient Discharge Summaries to an Ontology for Psychiatry.

Psychiatry aims at detecting symptoms, providing diagnoses and treating mental disorders. We developed ONTOPSYCHIA, an ontology for psychiatry in three modules: social and environmental factors of mental disorders, mental disorders, and treatments. The use of ONTOPSYCHIA, associated with dedicated tools, will facilitate semantic research in Patient Discharge Summaries (PDS). To develop the first module of the ontology we propose a PDS text analysis in order to explicit psychiatry concepts. We decided to set aside classifications during the construction of the modu le, to focus only on the information contained in PDS (bottom-up approach) and to return to domain classifications solely for the enrichment phase (top-down approach). Then, we focused our work on the development of the LOVMI methodology (Les Ontologies Validées par Méthode Interactive - Ontologies Validated by Interactive Method), which aims to provide a methodological framework to validate the structure and the semantic of an ontology

Use of electronic health records to evaluate practice individualization

Medical decisions are usually evaluated by checking their compliance with guidelines. We propose an approach to determine to which extent and how decisions are individualized to patients’ particular needs, beyond or against guidelines. For this purpose, we have to link the content of electronic health records with a specific ontology to allow formal and detailed representations of cases