Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Prosafe: a european endeavor to improve quality of critical care medicine in seven countries

BACKGROUND: long-lasting shared research databases are an important source of epidemiological information and can promote comparison between different healthcare services. Here we present ProsaFe, an advanced international research network in intensive care medicine, with the focus on assessing and improving the quality of care. the project involved 343 icUs in seven countries. all patients admitted to the icU were eligible for data collection. MetHoDs: the ProsaFe network collected data using the same electronic case report form translated into the corresponding languages. a complex, multidimensional validation system was implemented to ensure maximum data quality. individual and aggregate reports by country, region, and icU type were prepared annually. a web-based data-sharing system allowed participants to autonomously perform different analyses on both own data and the entire database. RESULTS: The final analysis was restricted to 262 general ICUs and 432,223 adult patients, mostly admitted to Italian units, where a research network had been active since 1991. organization of critical care medicine in the seven countries was relatively similar, in terms of staffing, case mix and procedures, suggesting a common understanding of the role of critical care medicine. conversely, icU equipment differed, and patient outcomes showed wide variations among countries. coNclUsioNs: ProsaFe is a permanent, stable, open access, multilingual database for clinical benchmarking, icU self-evaluation and research within and across countries, which offers a unique opportunity to improve the quality of critical care. its entry into routine clinical practice on a voluntary basis is testimony to the success and viability of the endeavor

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press