The MeSH-gram Neural Network Model: Extending Word Embedding Vectors with MeSH Concepts for UMLS Semantic Similarity and Relatedness in the Biomedical Domain

Eliciting semantic similarity between concepts in the biomedical domain remains a challenging task. Recent approaches founded on embedding vectors have gained in popularity as they risen to efficiently capture semantic relationships The underlying idea is that two words that have close meaning gather similar contexts. In this study, we propose a new neural network model named MeSH-gram which relies on a straighforward approach that extends the skip-gram neural network model by considering MeSH (Medical Subject Headings) descriptors instead words. Trained on publicly available corpus PubMed MEDLINE, MeSH-gram is evaluated on reference standards manually annotated for semantic similarity. MeSH-gram is first compared to skip-gram with vectors of size 300 and at several windows contexts. A deeper comparison is performed with tewenty existing models. All the obtained results of Spearman's rank correlations between human scores and computed similarities show that MeSH-gram outperforms the skip-gram model, and is comparable to the best methods but that need more computation and external resources.Comment: 6 pages, 2 table

Taxonomy of usage problems for improving user-centric online health information provision

Consumer health information portals (HIP) are a popular means to provide quality health information via the Web. However complex usage problems in HIPs are still a major barrier to their success. A usage-driven approach, which places emphasis on improving online services based on learnings from the data of the interactions between users and the system, is crucial to ensuring sustainable and user-centred online health provision. Inspired by this idea, we present a taxonomy of usage problems that encompasses the dimensions of the content, the systems and users, focusing on a holistic understanding of usage problems. Our taxonomy is grounded on a literature analysis empirically validated through an analysis of usage-data captured from a consumer health information portal, operational for the past five years. By exploring how usage data highlights user problems, we also present strategies for health portal improvements based on better understandings of usage data. Benefits of usage-driven health portals in terms of smart learning capabilities to improve content and user satisfaction are discussed

Taxonomy of Usage Issues for Consumer-centric Online Health Information Provision

Consumers are increasingly using Internet portals when searching for relevant health information. Despite the broad range of health information portals (HIPs) available, usage problems with such portals are still widely recognized and reported. In this study, we analyzed usage data from an operational health information portal and identified ways in which these problems can be addressed. While previous usage data and log analysis research has focused more on user behaviors, query structures, and human-computer interaction issues, this study covers more comprehensive issues such as content. We describe a taxonomy of usage issues derived from a literature analysis. We describe how we validated and refined the taxonomy based on analyzing the usage data from an operational health portal. Findings from the usage data indicate that a range of content issues exist that lead to unsuccessful searches. The analysis also highlights that users’ ineffective information seeking strategies are not well supported by the system’s design. We use this taxonomy to propose a usage-driven, consumer-centered approach for dynamic improvements of HIPs. We also discuss the study’s limitations and directions for future research

Combining different standards and different approaches for health information retrieval in a quality-controlled gateway.

International audienceInternet as source of information is increasing in preeminence in numerous fields, including health. We describe in this paper the CISMeF project (acronym of Catalogue and Index of French-speaking Medical Sites) which has been designed to help the health information consumers and health professionals to find what they are looking for among the numerous health documents available online. The catalogue is founded on two standards: a set of metadata and a terminology based on the MeSH thesaurus which has the same structure and use as an ontology of the medical domain. The structure of the catalogue allows us to place the project at an overlap between the present Web, which is informal, and the forthcoming Semantic Web. Many features of information retrieval and navigation through the catalogue were developed. These features take into account the kind of the end-user (health professional, medical student, patient). The CISMeF-patients catalogue is a sub-catalogue of CISMeF and is dedicated to the patients and the general public. It shares the same model as CISMeF whereas MEDLINE and MedlinePlus do not. We also propose to couple two approaches (morphological processing and data mining) to help the users by correcting and refining their queries