Search and Graph Database Technologies for Biomedical Semantic Indexing: Experimental Analysis (Preprint)

Abstract

BACKGROUND Biomedical semantic indexing is a very useful support tool for human curators in their efforts for indexing and cataloging the biomedical literature. OBJECTIVE The aim of this study was to describe a system to automatically assign Medical Subject Headings (MeSH) to biomedical articles from MEDLINE. METHODS Our approach relies on the assumption that similar documents should be classified by similar MeSH terms. Although previous work has already exploited the document similarity by using a k-nearest neighbors algorithm, we represent documents as document vectors by search engine indexing and then compute the similarity between documents using cosine similarity. Once the most similar documents for a given input document are retrieved, we rank their MeSH terms to choose the most suitable set for the input document. To do this, we define a scoring function that takes into account the frequency of the term into the set of retrieved documents and the similarity between the input document and each retrieved document. In addition, we implement guidelines proposed by human curators to annotate MEDLINE articles; in particular, the heuristic that says if 3 MeSH terms are proposed to classify an article and they share the same ancestor, they should be replaced by this ancestor. The representation of the MeSH thesaurus as a graph database allows us to employ graph search algorithms to quickly and easily capture hierarchical relationships such as the lowest common ancestor between terms. RESULTS Our experiments show promising results with an F1 of 69% on the test dataset. CONCLUSIONS To the best of our knowledge, this is the first work that combines search and graph database technologies for the task of biomedical semantic indexing. Due to its horizontal scalability, ElasticSearch becomes a real solution to index large collections of documents (such as the bibliographic database MEDLINE). Moreover, the use of graph search algorithms for accessing MeSH information could provide a support tool for cataloging MEDLINE abstracts in real time.

An Investigation of Recurrent Neural Architectures for Drug Name Recognition

“…The system that ranked first, WBI-NER (Rocktäschel et al, 2013), adopted very specialized features derived from an improved version of the ChemSpot tool (Rocktäschel et al, 2012), a collection of drug dictionaries and ontologies. Similarly, many other recent approaches (Abacha et al, 2015;Liu et al, 2015b;Segura-Bedmar et al, 2015) have been based on various combinations of general and domain-specific features.…”

“…Table 2 summarizes the basic statistics of the training and test datasets used in our experiments. For proper comparison, we follow the same settings as (Segura-Bedmar et al, 2015), using the training data of the DNR task along with the test data for the DDI task for training and validation of DNR. We split this joint dataset into a training and validation sets with approximately 70% of sentences for training and the remaining for validation.…”

“…Precision Recall F 1 Score Precision Recall F 1 Score WBI-NER (Rocktäschel et al, 2013) 88.00 87.00 87.80 61.00 56.00 58.10 Hybrid-DDI (Abacha et al, 2015) 93.00 70.00 80.00 74.00 25.00 37.00 Word2Vec+DINTO (Segura-Bedmar et al, 2015) 69 {50, 100, 300, 500, 1000}. Two additional parameters, the learning and drop-out rates, were sampled from a uniform distribution in the range [0.05, 0.1].…”

“…Conversely, machine-learning approaches have the potential to overcome all these limitations since their foundations are intrinsically robust to variants. The current state-of-the-art machine learning approaches follow a two-step process of feature engineering and classification (Segura-Bedmar et al, 2015;Abacha et al, 2015;Rocktäschel et al, 2013). Feature engineering refers to the task of representing text by dedicated numeric vectors using domain knowledge.…”