Elian Bourdin | Facultad de Ingenieria (original) (raw)
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Sanjay Gandhi Postgraduate Institute of Medician Sciences, Lucknow
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Papers by Elian Bourdin
The automatic conversion of free text into a medical ontology can allow computational access to i... more The automatic conversion of free text into a medical ontology can allow computational access to important information currently locked within clinical notes and patient reports. This system introduces a new method for automatically identifying medical concepts from the SNOMED Clinical Terminology in free text in near real time. The system presented consists of 3 modules; an Augmented Lexicon, term compositor and negation detector. The Augmented Lexicon indexes the SNOMED-CT terms, the term compositor finds qualification relationships between concepts and the negation detector identifies negative concepts. The system delivers the services through a variety of interfaces including direct programming access and web-based access. It is currently in use in a hospital environment to capture patient data response with SNOMED-CT codes in real time at the point of care. No strict evaluation has been done on the system to date, however preliminary results indicate performance within acceptable time and accuracy limits.
Clinical terminologies are considered a key technology for capturing clinical data in a precise a... more Clinical terminologies are considered a key technology for capturing clinical data in a precise and standardized manner, which is critical to accurately exchange information among different applications, medical records and decision support systems. An important step to promote the real use of clinical terminologies, such as SNOMED-CT, is to facilitate the process of finding mappings between local terms of medical records and concepts of terminologies. In this paper, we propose a mapping tool to discover text-toconcept mappings in SNOMED-CT. Name-based techniques were combined with a query expansion system to generate alternative search terms, and with a strategy to analyze and take advantage of the semantic relationships of the SNOMED-CT concepts. The developed tool was evaluated and compared to the search services provided by two SNOMED-CT browsers. Our tool automatically mapped clinical terms from a Spanish glossary of procedures in pathology with 88.0% precision and 51.4% recall, providing a substantial improvement of recall (28% and 60%) over other publicly accessible mapping services. The improvements reached by the mapping tool are encouraging. Our results demonstrate the feasibility of accurately mapping clinical glossaries to SNOMED-CT concepts, by means a combination of structural, query expansion and named-based techniques. We have shown that SNOMED-CT is a great source of knowledge to infer synonyms for the medical domain. Results show that an automated query expansion system overcomes the challenge of vocabulary mismatch partially.
SPARQL queries are a powerful method for querying the large and increasing number of linked open ... more SPARQL queries are a powerful method for querying the large and increasing number of linked open data repositories available through the Semantic Web. However , generating SPARQL queries can be difficult, even for experts. Interfaces that accept questions in natural language and convert them to SPARQL queries are one solution to this problem. We describe the Linked Open Data Question Answering (LODQA) system. LODQA is developed to generate SPARQL queries from natural language, with the goal of providing an easy-to-use interface to search linked open RDF data. The paper presents a prototype version of LODQA which works on SNOMED CT, discussing the design and implementation, together with the limitations of the current implementation and future directions for improvement.
The automatic conversion of free text into a medical ontology can allow computational access to i... more The automatic conversion of free text into a medical ontology can allow computational access to important information currently locked within clinical notes and patient reports. This system introduces a new method for automatically identifying medical concepts from the SNOMED Clinical Terminology in free text in near real time. The system presented consists of 3 modules; an Augmented Lexicon, term compositor and negation detector. The Augmented Lexicon indexes the SNOMED-CT terms, the term compositor finds qualification relationships between concepts and the negation detector identifies negative concepts. The system delivers the services through a variety of interfaces including direct programming access and web-based access. It is currently in use in a hospital environment to capture patient data response with SNOMED-CT codes in real time at the point of care. No strict evaluation has been done on the system to date, however preliminary results indicate performance within acceptable time and accuracy limits.
Clinical terminologies are considered a key technology for capturing clinical data in a precise a... more Clinical terminologies are considered a key technology for capturing clinical data in a precise and standardized manner, which is critical to accurately exchange information among different applications, medical records and decision support systems. An important step to promote the real use of clinical terminologies, such as SNOMED-CT, is to facilitate the process of finding mappings between local terms of medical records and concepts of terminologies. In this paper, we propose a mapping tool to discover text-toconcept mappings in SNOMED-CT. Name-based techniques were combined with a query expansion system to generate alternative search terms, and with a strategy to analyze and take advantage of the semantic relationships of the SNOMED-CT concepts. The developed tool was evaluated and compared to the search services provided by two SNOMED-CT browsers. Our tool automatically mapped clinical terms from a Spanish glossary of procedures in pathology with 88.0% precision and 51.4% recall, providing a substantial improvement of recall (28% and 60%) over other publicly accessible mapping services. The improvements reached by the mapping tool are encouraging. Our results demonstrate the feasibility of accurately mapping clinical glossaries to SNOMED-CT concepts, by means a combination of structural, query expansion and named-based techniques. We have shown that SNOMED-CT is a great source of knowledge to infer synonyms for the medical domain. Results show that an automated query expansion system overcomes the challenge of vocabulary mismatch partially.
SPARQL queries are a powerful method for querying the large and increasing number of linked open ... more SPARQL queries are a powerful method for querying the large and increasing number of linked open data repositories available through the Semantic Web. However , generating SPARQL queries can be difficult, even for experts. Interfaces that accept questions in natural language and convert them to SPARQL queries are one solution to this problem. We describe the Linked Open Data Question Answering (LODQA) system. LODQA is developed to generate SPARQL queries from natural language, with the goal of providing an easy-to-use interface to search linked open RDF data. The paper presents a prototype version of LODQA which works on SNOMED CT, discussing the design and implementation, together with the limitations of the current implementation and future directions for improvement.