NLP techniques associated with the OpenGALEN ontology for semi-automatic textual extraction of medical knowledge: abstracting and mapping equivalent linguistic and logical constructs (original) (raw)
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Methodology to build medical ontology from textual resources
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, 2006
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: Methodological Propositions
2005
In the medical field, it is now established that the maintenance of unambiguous thesauri is accomplished by the building of ontologies. Our task in the PertoMed project is to help pneumologists code acts and diagnoses with a software that represents medical knowledge by an ontology of the concerned specialty. We apply natural language processing tools to corpora to develop the resources needed to build this ontology. In this paper, our objective is to develop a methodology for the knowledge engineer to build various types of medical ontologies based on terminology extraction from texts according to the differential semantics theory. Our main research hypothesis concerns the joint use of two methods: distributional analysis and recognition of semantic relationships by lexico-syntactic patterns. The expected result is the building of an ontology of pneumology.
1997
Natural language understanding systems have to exploit various kinds of knowledge to be able to represent the meaning behind texts. Getting this knowledge in place is often such a huge enterprise that it is tempting to look for systems that can discover such knowledge automatically. In this paper, we describe how the distinction between conceptual and linguistic semantics probably can assist in reaching this objective, provided that distinguishing between them is not done to rigorously. We present several toy examples to support this view and argue that in a multilingual environment linguistic ontologies should be designed as interfaces between domain conceptualisations and linguistic knowledge bases.
Issues in the Structuring and Acquisition of an Ontology for Medical Language Understanding
Methods of Information in Medicine, 1995
Medical natural language understanding basically aims at representing the contents of medical texts in a formal, conceptual representation. The understanding process itself increasingly relies on a body of domain knowledge, generally expressed in the same conceptual formalism. The design of such a conceptual representation is a key knowledge acquisition issue. When representing knowledge, the most important point is to ensure that the formal exploitation of the knowledge representation conforms to its meaning in the domain. In this paper, we examine some methodological and theoretical principles to enforce this conformity. These principles result from our experience in Menelas, a medical language understanding project. 1
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. , Mobile: 06 62 77 59 85 4 The ICD-10 thesaurus is a medical and economic code for patients and allows analysing the activity of hospitals via diagnostic related groups. The French CCAM thesaurus is a complete and new classification of medical acts. 6
Computers in Biology and Medicine, 2006
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.
A natural language understanding system for knowledge-based analysis of medical texts
2004
An approach to knowledge-based understanding of real-world texts from the medical domain (viz. gastro-intestinal findings) is presented. We survey major methodological features of an object-oriented, fully lexicalized, dependency-based grammar model which is tightly linked to domain knowledge representations based on description logics. The parser adheres to the principles of robustness, incrementality and concurrency. The substrate of automatic knowledge acquisition are text knowledge bases generated by the parser from medical narratives, which represent major portions of the content of these documents.
From syntactic-semantic tagging to knowledge discovery in medical texts
International Journal of Medical Informatics, 1998
In the GALEN project, the syntactic-semantic tagger MultiTALE is upgraded to extract knowledge from natural language surgical procedure expressions. In this paper, we describe the methodology applied and show that out of a randomly selected sample of such expressions coming from the procedure axis of Snomed International, 81% could be analysed correctly. The problems encountered fall in three different categories: unusual grammatical configurations within the Snomed terms, insufficient domain knowledge and different categorisation of concepts and semantic links in the domain and linguistic models used. It is concluded that the Multi-TALE system can be used to attach meaning to words that not have been encountered previously, but that an interface ontology mediating between domain models and linguistic models is needed to arrive at a higher level of independence from both particular languages and from particular domains.
Clinicians develops their own jargon to report about their work. Most EHR systems provide a way to extract reports of their daily activity. In order to formalize an automated acquisition from free-form, natural language texts in Portuguese into a Clinical Practice Ontology an important step that is involved is to develop the ability of "understanding" all the nicknames, acronyms and short-hand forms that each clinician tend to write down in their reports. We present the steps to develop personal clinical vocabularies extracting directly from SOAP clinical reports in Portuguese. All the presented techniques are easily developed for any other natural language or knowledge representation framework with the due adaptations.