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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. [ABSTRACT FROM AUTHOR]

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 crossvalidation 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. [ABSTRACT FROM AUTHOR]

Background Patients are using online health forums (OHFs) to exchange about their health, diseases and treatments. Some OHFs are independent, not submitted to health care providers' control nor to social desirability. This must probably influence the very nature of patient's learning. Objectives: This research aims to bring some answers to the following questions: What are the characteristics of independent OHFs? What are OHF internaut patients' profile? How do they use OHFs? Are there any characteristics that might help to understand their use of OHFs? Methods: A literature review has been undertaken querying 10 databases. The review was completed with an analysis of the "grey literature". Eleven keywords were used singly or in combination. The article selection was conducted by two independent researchers. Results: 23 documents were selected and analysed. Five OHF definitions, three user's classifications and four OHF uses were identified and analysed. Discussion: The review allowed us to 1) formulate a definition of a chronic health conditions oriented OHF, 2) describe its characteristics, 3) suggest a users' classification and, 4) describe the four OHF uses. Conclusions: Independent OHF is a virtual and free space beyond institutional control. It may be considered as a learning and educational media, formal and informal, and spontaneous. The understanding of the knowledge acquisition processes and strategies among OHF users is necessary in order to use this web tool as patient education strategy. [ABSTRACT FROM AUTHOR]

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 supervised 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. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including computational terminology, natural language processing (NLP) and information retrieval.

Recent and intensive research in the biomedical area enabled to accumulate and disseminate biomedical knowledge through various knowledge bases increasingly available on the Web. The exploitation of this knowledge requires to create links between these bases and to use them jointly. Linked Data, the SPARQL language and interfaces in natural language question answering provide interesting solutions for querying such knowledge bases. However, while using biomedical Linked Data is crucial, life-science researchers may have difficulties using the SPARQL language. Interfaces based on natural language question answering are recognized to be suitable for querying knowledge bases. In this paper, we propose a method for translating natural language questions into SPARQL queries.We use Natural Language Processing tools, semantic resources and RDF triple descriptions. We designed a four-step method which allows to linguistically and semantically annotate questions, to perform an abstraction of these questions, then to build a representation of the SPARQL queries, and finally to generate the queries. The method is designed on 50 questions over three biomedical knowledge bases used in the task 2 of the QALD-4 challenge framework and evaluated on 27 new questions. It achieves good performance with 0.78 F-measure on the test set. The method for translating questions into SPARQL queries is implemented as a Perl module and is available at http://search.cpan.org/~thhamon/ RDF-NLP-SPARQLQuery/. [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. [ABSTRACT FROM AUTHOR]

Copyright of Traitement Automatique des Langues is the property of Association pour le Traitement Automatique des Langues (ATALA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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 [ABSTRACT FROM AUTHOR]

An introduction is presented in which the author discusses topics within the issue including proposal to improve performance of automatic term recognition, multilingual terminology acquisition from comparable corpora and development of sociopolitical thesaurus in Russian.

Copyright of Traitement Automatique des Langues is the property of Association pour le Traitement Automatique des Langues (ATALA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

Pharmacovigilance is the activity related to the collection, analysis and prevention of adverse drug reactions (ADRs) induced by drugs. This activity is usually performed within dedicated databases (national, European, international...), in which the ADRs declared for patients are usually coded with a specific controlled terminology MedDRA (Medical Dictionary for Drug Regulatory Activities). Traditionally, the detection of adverse drug reactions is performed with data mining algorithms, while more recently the groupings of close ADR terms are also being exploited. The Standardized MedDRA Queries (SMQs) have become a standard in pharmacovigilance. They are created manually by international boards of experts with the objective to group together the MedDRA terms related to a given safety topic. Within the MedDRA version 13, 84 SMQs exist, although several important safety topics are not yet covered. The objective of our work is to propose an automatic method for assisting the creation of SMQs using the clustering of semantically close MedDRA terms. The experimented method relies on semantic approaches: semantic distance and similarity algorithms, terminology structuring methods and term clustering. The obtained results indicate that the proposed unsupervised methods appear to be complementary for this task, they can generate subsets of the existing SMQs and make this process systematic and less time consuming. [ABSTRACT FROM AUTHOR]

Abstract: Objective: Automatically analyze the online discussions related to diabetes and extract information on patient skills for managing this disease. Methods: Two collections of about 7000 and 23,000 messages from online discussion fora and 174 skills from an available taxonomy are processed with Natural Language Processing methods and semantically enriched. Skills are projected on the messages to detect those skills which are mentioned by patients. Quantitative and qualitative evaluation is performed. Results: The method recognizes almost all the aimed skills in fora. The quality of the skills’ recognition varies with the method's parameters. Most of the selected messages are relevant to at least one of the associated skills. Manual analysis shows a substantial number of messages is dedicated to daily self-care and psychosocial skills. Conclusion: Study of real exchanges between patients leads to a better understanding of their skills in daily self-management of diabetes. Practice implications: Our experiments can be useful for a better understanding and better knowledge of self-management of diseases by patients. They can also refine existing patient education programs. [Copyright &y& Elsevier]

Medical entity recognition is currently generally performed by data-driven methods based on supervised machine learning. Expert-based systems, where linguistic and domain expertise are directly provided to the system are often combined with data-driven systems. We present here a case study where an existing expert-based medical entity recognition system, Ogmios, is combined with a data-driven system, Caramba, based on a linear-chain Conditional Random Field (CRF) classifier. Our case study specifically highlights the risk of overfitting incurred by an expert-based system. We observe that it prevents the combination of the 2 systems from obtaining improvements in precision, recall, or F-measure, and analyze the underlying mechanisms through a post-hoc feature-level analysis. Wrapping the expert-based system alone as attributes input to a CRF classifier does boost its F-measure from 0.603 to 0.710, bringing it on par with the data-driven system. The generalization of this method remains to be further investigated. [ABSTRACT FROM AUTHOR]

Since there is a great confusion between the ontologies and other semantic resources, the purpose of this special issue is to address the question on 'Ontologies and terminologies: Continuum or dichotomy'. We have selected five articles which study the differences and similarities between these semantic resources. More particularly, the articles are dedicated to the differences existing at the level of terms and of relations, the use of the ontologies on corpora and the dynamic and static representation of the knowledge. [ABSTRACT FROM AUTHOR]

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]

Automated subject classification has been a challenging research issue for many years now, receiving particular attention in the past decade due to rapid increase of digital documents. The most frequent approach to automated classification is machine learning. It, however, requires training documents and performs well on new documents only if these are similar enough to the former. We explore a string-matching algorithm based on a controlled vocabulary, which does not require training documents--instead it reuses the intellectual work put into creating the controlled vocabulary. Terms from the Engineering Information thesaurus and classification scheme were matched against title and abstract of engineering papers from the Compendex database. Simple string-matching was enhanced by several methods such as term weighting schemes and cut-offs, exclusion of certain terms, and enrichment of the controlled vocabulary with automatically extracted terms. The best results are 76% recall when the controlled vocabulary is enriched with new terms, and 79% precision when certain terms are excluded. Precision of individual classes is up to 98%. These results are comparable to state-of-the-art machine-learning algorithms. [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]

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.

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.