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

Clinico-epidemiological profile and outcomes of adults with COVID-19: A hospital-based retrospective study in Kerala, India

Introduction: The clinical and epidemiological presentations of patients with coronavirus disease 2019 (COVID-19) in India is still not well explored. We studied the epidemiological and clinical profile and outcomes of COVID-19 patients admitted to a tertiary care private hospital in Kerala, India. Methods: In this retrospective study, we analyzed data of 476 adult (≥18 years) COVID-19 patients admitted to a tertiary care hospital in Kerala from September 1, 2020 to March 31, 2021. The patients were categorized into mild, moderate, and severe cases and followed till discharge or death. Data were analyzed using Statistical Package for the Social Sciences (SPSS) version 23.0 with a significance set at P < 0.05. Results: The median age was 57 years (56% men). Mild, moderate, and severe cases accounted for 17%, 65%, and 18%, respectively. Around 75% had at least one comorbidity, and 51% had multiple comorbidities. The most common comorbidities were diabetes (45%), hypertension (44%), dyslipidemia (15%), and cardiac problems (12%). The elevated D-dimer values among patients in different categories were significantly different, with 74% in severe, 46% in moderate, and 19% in mild category patients. Serum ferritin, C-reactive protein, lactic acid dehydrogenase, and neutrophil to lymphocyte ratio values were significantly higher for severely ill patients. Thirty deaths (67% men) occurred during the study period, with a case fatality rate of 6.3%. Mortality mainly happened in the older age group (80%) and those with multimorbidity (90%). Conclusion: Age and multimorbidity are the major contributing factors for death in hospitalized COVID-19 patients. Generalization of the findings necessitates well-designed large-scale studies

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