A manually annotated corpus in French for the study of urbanization and the natural risk prevention

Land artificialization is a serious problem of civilization. Urban planning and natural risk management are aimed to improve it. In France, these practices operate the Local Land Plans (PLU - Plan Local d'Urbanisme) and the Natural risk prevention plans (PPRn - Plan de Prévention des Risques naturels) containing land use rules. To facilitate automatic extraction of the rules, we manually annotated a number of those documents concerning Montpellier, a rapidly evolving agglomeration exposed to natural risks. We defined a format for labeled examples in which each entry includes title and subtitle. In addition, we proposed a hierarchical representation of class labels to generalize the use of our corpus. Our corpus, consisting of 1934 textual segments, each of which labeled by one of the 4 classes (Verifiable, Non-verifiable, Informative and Not pertinent) is the first corpus in the French language in the fields of urban planning and natural risk management. Along with presenting the corpus, we tested a state-of-the-art approach for text classification to demonstrate its usability for automatic rule extraction.

An annotated dataset for event-based surveillance of antimicrobial resistance

This paper presents an annotated dataset used in the MOOD Antimicrobial Resistance (AMR) hackathon, hosted in Montpellier, June 2022. The collected data concerns unstructured data from news items, scientific publications and national or international reports, collected from four event-based surveillance (EBS) Systems, i.e. ProMED, PADI-web, HealthMap and MedISys. Data was annotated by relevance for epidemic intelligence (EI) purposes with the help of AMR experts and an annotation guideline. Extracted data were intended to include relevant events on the emergence and spread of AMR such as reports on AMR trends, discovery of new drug-bug resistances, or new AMR genes in human, animal or environmental reservoirs. This dataset can be used to train or evaluate classification approaches to automatically identify written text on AMR events across the different reservoirs and sectors of One Health (i.e. human, animal, food, environmental sources, such as soil and waste water) in unstructured data (e.g. news, tweets) and classify these events by relevance for EI purposes.

Could spatial features help the matching of textual data?

Textual data is available to an increasing extent through different media (social networks, companies data, data catalogues, etc.). New information extraction methods are needed since these new resources are highly heterogeneous. In this article, we propose a text matching process based on spatial features and assessed through heterogeneous textual data. Besides being compatible with heterogeneous data, it comprises two contributions: first, spatial information is extracted for comparison purposes and subsequently stored in a dedicated spatial textual representation (STR); and then two transformations are applied on STR to improve the spatial similarity estimation. This article outlines the proposed approach with new contributions: (i) a new geocoding methods using general co-occurrences between entities, and (ii) a thorough evaluation followed by (iii) an in-depth discussion. The results obtained on two corpora demonstrate that good spatial matches (≈ 80% precision on major criteria) can be obtained between the most similar STRs with further enhancement achieved via STR transformation.

A novel framework for biomedical entity sense induction

BACKGROUND

Rapid advancements in biomedical research have accelerated the number of relevant electronic documents published online, ranging from scholarly articles to news, blogs, and user-generated social media content. Nevertheless, the vast amount of this information is poorly organized, making it difficult to navigate. Emerging technologies such as ontologies and knowledge bases (KBs) could help organize and track the information associated with biomedical research developments. A major challenge in the automatic construction of ontologies and KBs is the identification of words with its respective sense(s) from a free-text corpus. Word-sense induction (WSI) is a task to automatically induce the different senses of a target word in the different contexts. In the last two decades, there have been several efforts on WSI. However, few methods are effective in biomedicine and life sciences.

METHODS

We developed a framework for biomedical entity sense induction using a mixture of natural language processing, supervised, and unsupervised learning methods with promising results. It is composed of three main steps: (1) a polysemy detection method to determine if a biomedical entity has many possible meanings; (2) a clustering quality index-based approach to predict the number of senses for the biomedical entity; and (3) a method to induce the concept(s) (i.e., senses) of the biomedical entity in a given context.

RESULTS

To evaluate our framework, we used the well-known MSH WSD polysemic dataset that contains 203 annotated ambiguous biomedical entities, where each entity is linked to 2-5 concepts. Our polysemy detection method obtained an F-measure of 98%. Second, our approach for predicting the number of senses achieved an F-measure of 93%. Finally, we induced the concepts of the biomedical entities based on a clustering algorithm and then extracted the keywords of reach cluster to represent the concept.

CONCLUSIONS

We have developed a framework for biomedical entity sense induction with promising results. Our study results can benefit a number of downstream applications, for example, help to resolve concept ambiguities when building Semantic Web KBs from biomedical text.

Mining local climate data to assess spatiotemporal dengue fever epidemic patterns in French Guiana

OBJECTIVE

To identify local meteorological drivers of dengue fever in French Guiana, we applied an original data mining method to the available epidemiological and climatic data. Through this work, we also assessed the contribution of the data mining method to the understanding of factors associated with the dissemination of infectious diseases and their spatiotemporal spread.

METHODS

We applied contextual sequential pattern extraction techniques to epidemiological and meteorological data to identify the most significant climatic factors for dengue fever, and we investigated the relevance of the extracted patterns for the early warning of dengue outbreaks in French Guiana.

RESULTS

The maximum temperature, minimum relative humidity, global brilliance, and cumulative rainfall were identified as determinants of dengue outbreaks, and the precise intervals of their values and variations were quantified according to the epidemiologic context. The strongest significant correlations were observed between dengue incidence and meteorological drivers after a 4-6-week lag.

DISCUSSION

We demonstrated the use of contextual sequential patterns to better understand the determinants of the spatiotemporal spread of dengue fever in French Guiana. Future work should integrate additional variables and explore the notion of neighborhood for extracting sequential patterns.

CONCLUSIONS

Dengue fever remains a major public health issue in French Guiana. The development of new methods to identify such specific characteristics becomes crucial in order to better understand and control spatiotemporal transmission.

Decision making environment on Rift Valley fever in Ferlo (Senegal)

The Rift Valley fever (RVF), which first appeared in Kenya in 1912, is an anthropozoonosis widespread in tropical areas. In Senegal, it is particularly felt in the Ferlo area where a strong presence of ponds shared by humans, cattle and vectors is noted. As part of the studies carried out on the environmental factors which favour its start and propagation, the focus of this paper is put on the decision making process to evaluate the impacts, the interactions and to make RVF monitoring easier. The present paper proposes a model based on data mining techniques and dedicated to trade experts. This model integrates all the involved data and the results of the analyses made on the characteristics of the surrounding ponds. This approach presents some advantage in revealing the relationship between environmental factors and RVF transmission vectors for space-time epidemiology monitoring purpose.

Towards a Mixed Approach to Extract Biomedical Terms from Text Corpus

The objective of this paper is to present a methodology to extract and rank automatically biomedical terms from free text. The authors present new extraction methods taking into account linguistic patterns specialized for the biomedical domain, statistic term extraction measures such as C-value and statistic keyword extraction measures such as Okapi BM25, and TFIDF. These measures are combined in order to improve the extraction process and the authors investigate which combinations are the more relevant associated to different contexts. Experimental results show that an appropriate harmonic mean of C-value associated to keyword extraction measures offers better precision, both for single-word and multi-words term extraction. Experiments describe the extraction of English and French biomedical terms from a corpus of laboratory tests available online. The results are validated by using UMLS (in English) and only MeSH (in French) as reference dictionary.

The epidemiologic surveillance of dengue-fever in French Guiana: when achievements trigger higher goals

The epidemiology of dengue fever in French Guiana is marked by a combination of permanent transmission of the virus in the whole country and the occurrence of regular epidemics. Since 2006, a multi data source surveillance system was implemented to monitor dengue fever patterns, to improve early detection of outbreaks and to allow a better provision of information to health authorities, in order to guide and evaluate prevention activities and control measures. This report illustrates the validity and the performances of the system. We describe the experience gained by such a surveillance system and outline remaining challenges. Future works will consist in the use of other data sources such as environmental factors in order to improve knowledge on virus transmission mechanisms and determine how to use them for outbreaks prediction.

Discovering novelty in sequential patterns: application for analysis of microarray data on Alzheimer disease

Analyzing microarrays data is still a great challenge since existing methods produce huge amounts of useless results. We propose a new method called NoDisco for discovering novelties in gene sequences obtained by applying data-mining techniques to microarray data.

METHOD

We identify popular genes, which are often cited in the literature, and innovative genes, which are linked to the popular genes in the sequences but are not mentioned in the literature. We also identify popular and innovative sequences containing these genes. Biologists can thus select interesting sequences from the two sets and obtain the k-best documents.

RESULTS

We show the efficiency of this method by applying it on real data used to decipher the mechanisms underlying Alzheimer disease.

CONCLUSION

The first selection of sequences based on popularity and innovation help experts focus on relevant sequences while the top-k documents help them understand the sequences.

GeneMining: identification, visualization, and interpretation of brain ageing signatures

Transcriptomic technologies are promising tools for identifying new genes involved in cerebral ageing or in neurodegenerative diseases such as Alzheimer's disease. These technologies produce massive biological data, which so far are extremely difficult to exploit. In this context, we propose GeneMining, a multidisciplinary methodology, which aims at developing new strategies to analyse such data, and to design interactive tools to help biologists to identify, visualize and interpret brain ageing signatures. In order to address the specific problem of brain ageing signatures discovery, we combine and apply existing tools with emphasis to a new efficient data mining method based on sequential patterns.