B iomed C urator: Data Curation for Biomedical Literature (2022)

Abstract: We present BiomedCurator1, a web application that extracts the structured data from scientific articles in PubMed and ClinicalTrials.gov. BiomedCurator uses state-of-the-art natural language processing techniques to fill the fields pre-selected by domain experts in the relevant biomedical area. The BiomedCurator web application includes: text generation based model for relation extraction, entity detection and recognition, text classification model for extracting several fields, information retrieval from external knowledge base to retrieve IDs, and a pattern-based extraction approach that can extract several fields using regular expressions over the PubMed and ClinicalTrials.gov datasets. Evaluation results show that different approaches of BiomedCurator web application system are effective for automatic data curation in the biomedical domain.

## introduction scientific article contains a lot of valuable information. for example, reports on clinical studies provide the pieces of information including the applied drug, the target disease, the dose, the dosing period, the ages of the human subjects, and the results. such pieces of information are useful in data mining and statistical analysis for drug discovery and drug development, if they are properly structured. we call this structurization process data curation in this paper, aiming at two-dimensional spreadsheet style structured data as illustrated in figure 1 . data curation is usually conducted by human experts, who are supposed to read and understand scientific papers, and fill in the spreadsheet. the purpose of this paper is to develop a web application system for automatic data curation in the biomedical domain, which we name biomedcurator. specifically, for a pubmed/clinicaltrials.gov id given by a user, biomedcurator returns values for 61 information pieces (henceforth, fields). the task of data curation requires a number of different nlp techniques including named entity recognition (ner), entity linking, relation extraction, and text classification. one notable characteristic of this task is that datasets curated by human experts provide spreadsheet-style supervision signal, but do not tell where in the paper each information piece is described; we cannot annotate bio tags to the paper unlike the training data for ner. one approach to perform automatic data curation is to use both structured data obtained from the literature and the original literature as training data. the advantage of this approach is that it can output important fields in a data format that is needed by intended users. on the other hand, disadvantages emerge, as typified by the following. (1) since only information that is important to intended users is included in the structured data, information that is important in nlp (e.g., where each data field is described in the original literature) tends to be omitted. (2) in the process of creating such structured data, the words are often bundled into a notation different from that used in the original literature for the correction of word distortions. in this study, we have developed a web application that can easily realize automated data curation by solving these technical issues with the methods described in section 2.2. ## biomedcurator: data curation system for biomedical domain we first describe the dataset for this task, and then the natural language processing techniques used in the system, followed by the description of our system as a web application. ## experimental settings in this section, we evaluate our system on our datasets. ## results and discussion in contrast, an article has only one published year information, and our system just needs to predict only one answer. so there are 2 possibilities: correct and incorrect. we compute the f-score for evaluating the fields that have multiple answers. for example, a certain document contains two gold answers and our system predicts one or more predictions. in the clinicaltrials.gov dataset, tables 4 and 5 show the performance of different fields in terms of f-score and accuracy. in these tables, the results show the extraction performances over most of the fields are good except duration, disease_sub_category, and bnamir are relatively very poor. the low performance of the field duration can be explained by the fact that field name accuracy (%) type of alteration 87.44 phenotype_alteration 93.00 significance 99.95 author_conclusion 100.00 title 95.33 year 99.61 ## conclusion we propose biomedcurator based on several data curation approaches from biomedical literature. our approach is distantly supervised based approach to create training data. besides, it follows the state-of-the-art nlp techniques that extracts the information from pubmed and clinicaltrials.gov articles and fill the 61 data fields. we also present an interactive web application of biomedcurator to facilitate the biomedical research. experimental results on two datasets show that biomedcurator performs very well to extract the template fields information in terms of both f-score and accuracy. the biomedcurator system is continually evolving; we will continue to improve the system as well as to implement new functions such as n-ary relation extraction to further facilitate biomedcurator research.