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Pharmacovigilance is the activity related to the collection, analysis and prevention of adverse drug reactions (ADRs) induced by drugs. It leads to the safety survey of pharmaceutical products. The pharmacovigilance process benefits from the traditional statistical approaches and also from the qualitative information on semantic relations between close ADR terms, such as SMQs or hierarchical levels of MedDRA. In this work, our objective is to detect the semantic relatedness between the ADR MedDRA terms. To achieve this, we combine two approaches: semantic similarity algorithms computed within structured resources and terminology structuring methods applied to a raw list of the MedDRA terms. We compare these methods between them and study their differences and complementarity. The results are evaluated against the gold standard manually compiled within the pharmacovigilance area and also with an expert. The combination of the methods leads to an improved recall. [ABSTRACT FROM AUTHOR]

Risk factors discovery and prevention is an active research field within the biomedical domain. Despite abundant existing information on risk factors, as found in bibliographical databases or on several websites, accessing this information may be difficult. Methods from Natural Language Processing and Information Extraction can be helpful to access it more easily. Specifically, we show a procedure for analyzing massive amounts of scientific literature and for detecting linguistically marked associations between pathologies and risk factors. This approach allowed us to extract over 22,000 risk factors and associated pathologies. The performed evaluations pointed out that (1) over 88% of risk factors for coronary heart disease are correct, (2) associated pathologies, when they could be compared to MeSH indexing, are correct in about 70%, and (3) in existing terminologies links between risk factors and their pathologies are seldom recorded. [ABSTRACT FROM AUTHOR]

Acquisition and enrichment of lexical resources is an important research area for the computational linguistics. We propose a method for inducing a lexicon of synonyms and for its weighting in order to establish its reliability. The method is based on the analysis of syntactic structure of complex terms. We apply and evaluate the approach on three biomedical terminologies (MeSH, Snomed Int, Snomed CT). Between 7.7 and 33.6% of the induced synonyms are ambiguous and cooccur with other semantic relations. A virtual reference allows to validate 9 to 14% of the induced synonyms. [ABSTRACT FROM AUTHOR]

Currently, the use of Natural Language Processing (NLP) approaches in order to improve search and exploration of electronic health records (EHRs) within healthcare information systems is not a common practice. One reason for this is the lack of suitable lexical resources: various types of such resources need to be collected or acquired. In this work, we propose a novel method for the acquisition of synonymous resources. This method is language-independent and relies on existence of structured terminologies. It enables to decipher hidden synonymous relations between simple words and terms on the basis of their syntactic analysis and exploitation of their compositionality. Applied to series of synonym terms from the French subset of the UMLS, the method shows 99% precision. The overlap between thus inferred terms and the existing sparse resources of synonyms is very low. [ABSTRACT FROM AUTHOR]

Extraction of information related to the medication is an important task within the biomedical area. Our method is applied to different types of documents in three languages. The results indicate that our approach can efficiently update and enrich the existing drug vocabularies. [ABSTRACT FROM AUTHOR]

We address the problem of semantic labeling of terms in two French medical corpora with the subset of the UMLS. We perform two experiments relying on the structure of words and terms, and on their context: 1) the semantic label of already identified terms is predicted; 2) the terms are detected in raw texts and their semantic label is predicted. Our results show over 0.90 F-measure.

This paper presents a prototype for the visualization of food-drug interactions implemented in the MIAM project, whose objective is to develop methods for the extraction and representation of these interactions and to make them available in the Thériaque database. The prototype provides users with a graphical visualization showing the hierarchies of drugs and foods in front of each other and the links between them representing the existing interactions as well as additional details about them, including the number of articles reporting the interaction. The prototype is interactive in the following ways: hierarchies can be easily folded and unfolded, a filter can be applied to view only certain types of interactions, and details about a given interaction are displayed when the mouse is moved over the corresponding link. Future work includes proposing a version more suitable for non-health professional users and the representation of the food hierarchy based on a reference classification.

Detection of difficult for understanding words is a crucial task for ensuring the proper understanding of medical texts such as diagnoses and drug instructions. We propose to combine supervised machine learning algorithms using various features with word embeddings which contain context information of words. Data in French are manually cross-annotated by seven annotators. On the basis of these data, we propose cross-validation scenarios in order to test the generalization ability of models to detect the difficulty of medical words. On data provided by seven annotators, we show that the models are generalizable from one annotator to another.

Exchanges between diabetic patients on discussion fora permit to study their understanding of their disorder, their behavior and needs when facing health problems. When analyzing these exchanges and behavior, it is necessary to collect information on user profile. We present an approach combining lexicon and super-vised classifiers for the identification of age and gender of contributors, their disorders and relation between contributor and patient. According to parameters of the method, precision is between 100% for gender and 53.48% for disorders.

This paper reports on Task 2 of the 2016 CLEF eHealth evaluation lab which extended the previous information extraction tasks of ShARe/CLEF eHealth evaluation labs. The task continued with named entity recognition and normalization in French narratives, as offered in CLEF eHealth 2015. Named entity recognition involved ten types of entities including disorders that were defined according to Semantic Groups in the Unified Medical Language System ® (UMLS ® ), which was also used for normalizing the entities. In addition, we introduced a large-scale classification task in French death certificates, which consisted of extracting causes of death as coded in the International Classification of Diseases, tenth revision (ICD10). Participant systems were evaluated against a blind reference standard of 832 titles of scientific articles indexed in MEDLINE, 4 drug monographs published by the European Medicines Agency (EMEA) and 27,850 death certificates using Precision, Recall and F-measure. In total, seven teams participated, including five in the entity recognition and normalization task, and five in the death certificate coding task. Three teams submitted their systems to our newly offered reproducibility track. For entity recognition, the highest performance was achieved on the EMEA corpus, with an overall F-measure of 0.702 for plain entities recognition and 0.529 for normalized entity recognition. For entity normalization, the highest performance was achieved on the MEDLINE corpus, with an overall F-measure of 0.552. For death certificate coding, the highest performance was 0.848 F-measure.

While patients can freely access their Electronic Health Records or online health information, they may not be able to correctly understand the content of these documents. One of the challenges is related to the difference between expert and non-expert languages. We propose to investigate this issue within the Information Retrieval field. The patient queries have to be associated with the corresponding expert documents, that provide trustworthy information. Our approach relies on a state-of-the-art IR system called Indri and on semantic resources. Different query expansion strategies are explored. Our system shows up to 0.6740 P@10, up to 0.7610 R@10, and up to 0.6793 NDCG@10.

With the recent and intensive research in the biomedical area, the knowledge accumulated is disseminated through various knowledge bases. Links between these knowledge bases are needed in order to use them jointly. Linked Data, SPARQL language, and interfaces in Natural Language question-answering provide interesting solutions for querying such knowledge bases. We propose a method for translating natural language questions in SPARQL queries. We use Natural Language Processing tools, semantic resources, and the RDF triples description. The method is designed on 50 questions over 3 biomedical knowledge bases, and evaluated on 27 questions. It achieves 0.78 F-measure on the test set. The method for translating natural language questions into SPARQL queries is implemented as Perl module available at http://search.cpan.org/ thhamon/RDF-NLP-SPARQLQuery.

Risk factors discovery and prevention is an active research field within the biomedical domain. Despite abundant existing information on risk factors, as found in bibliographical databases or on several websites, accessing this information may be difficult. Methods from Natural Language Processing and Information Extraction can be helpful to access it more easily. Specifically, we show a procedure for analyzing massive amounts of scientific literature and for detecting linguistically marked associations between pathologies and risk factors. This approach allowed us to extract over 22,000 risk factors and associated pathologies. The performed evaluations pointed out that (1) over 88% of risk factors for coronary heart disease are correct, (2) associated pathologies, when they could be compared to MeSH indexing, are correct in about 70%, and (3) in existing terminologies links between risk factors and their pathologies are seldom recorded.

Acquisition and enrichment of lexical resources is an important research area for the computational linguistics. We propose a method for inducing a lexicon of synonyms and for its weighting in order to establish its reliability. The method is based on the analysis of syntactic structure of complex terms. We apply and evaluate the approach on three biomedical terminologies (MeSH, Snomed Int, Snomed CT). Between 7.7 and 33.6% of the induced synonyms are ambiguous and cooccur with other semantic relations. A virtual reference allows to validate 9 to 14% of the induced synonyms.

The motivation of this work is to study the use of speculation markers within scientific writing: this may be useful for discovering whether these markers are regularly spread across biomedical articles and then for establishing the logical structure of articles. To achieve these objectives, we compute associations between article sections and speculation markers. We use machine learning algorithms to show that there are strong and interesting associations between speculation markers and article structure. For instance, strong markers, which strongly influence the presentation of knowledge, are specific to Results, Discussion and Abstract; while non strong markers appear with higher regularity within Material and Methods. Our results indicate that speculation is governed by observable usage rules within scientific articles and can help their structuring.

Objective: Currently, the use of natural language processing (NLP) approaches in order to improve search and exploration of electronic health records (EHRs) within healthcare information systems is not a common practice. One reason for this is the lack of suitable lexical resources. Indeed, in order to support such tasks, various types of such resources need to be collected or acquired (i.e., morphological, orthographic, synonymous)., Methods: We propose a novel method for the acquisition of synonymy resources. This method is language-independent and relies on existence of structured terminologies. It enables to decipher hidden synonymy relations between simple words and terms on the basis of their syntactic analysis and exploitation of their compositionality., Results: Applied to series of synonym terms from the French subset of the UMLS , the method shows 99% precision. The overlap between thus inferred terms and the existing sparse resources of synonyms is very low. In order to better integrate these resources in an EHR search system, we analyzed a sample of clinical queries submitted by healthcare professionals., Conclusions: Observation of clinical queries shows that they make a very little use of the query expansion function, and, whenever they do, synonymy relations are rarely involved.

Acquisition and enrichment of lexical resources is acknowledged as an important research in the area of computational linguistics. While such resources are often missing, specialized domains, ie biomedicine, propose several structured terminologies. In this paper, we propose a high-quality method for exploiting a structured terminology and inferring elementary synonym lexicon. The method is based on the analysis of syntactic structure of complex terms. The inferred synonym pairs are then profiled according to different clues endogenously computed within the same terminology. We apply and evaluate the approach on the Gene Ontology biomedical terminology.

Currently, the use of Natural Language Processing (NLP) approaches in order to improve search and exploration of electronic health records (EHRs) within healthcare information systems is not a common practice. One reason for this is the lack of suitable lexical resources: various types of such resources need to be collected or acquired. In this work, we propose a novel method for the acquisition of synonymous resources. This method is language-independent and relies on existence of structured terminologies. It enables to decipher hidden synonymous relations between simple words and terms on the basis of their syntactic analysis and exploitation of their compositionality. Applied to series of synonym terms from the French subset of the UMLS, the method shows 99% precision. The overlap between thus inferred terms and the existing sparse resources of synonyms is very low.