COVID-19 Kaggle Literature Organization

Eren, Maksim E.; Solovyev, Nick; Raff, Edward; Nicholas, Charles; Johnson, Ben

doi:10.1145/3395027.3419591

Cited by 10 publications

(19 citation statements)

References 22 publications

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“…In recent work, multiple groups such as [14] [15] and [16] have evaluated their models over the COVID-19 Open Research Dataset (CORD-19) [9] to help bridge the gap between researchers and the rapid growth of journal publications. In [14], authors test the efficacy of a graph-based clustering model and Bio-BERT [15] word embeddings approach for information retrieval through a Question-Answer bot related to clinical queries.…”

Section: Related Work a Automation Of Literature Reviewmentioning

confidence: 99%

“…The efforts from the Kaggle COVID-19 Literature Organization [9] to develop the CORD-19 dataset aim to resolve the limitations that scientists face when handling the evergrowing COVID-19 literature. Kaggle has coordinated with prestigious institutions and research companies to facilitate data analytics challenges.…”

Section: Expertise Sharing and Crowdsourcingmentioning

confidence: 99%

“…We validate the utility of our KnowCOVID-19 system using a collection of 10,0000 articles from the Kaggle COVID-19 Open Research Dataset (CORD-19) [9]. We conduct a two-fold case study to evaluate: (i) the effectiveness of our DSTM [6] in terms of the perplexity metric in our evidencebased filtering against state-of-the-art models to show an improved generalization performance as well as knowledge pattern query effectiveness considering the distribution of unobserved topics (e.g., drugs and genes) within the CORD-19 dataset, and (ii) the benefits of our information retrieval techniques using Term Frequency-Inverse Document Frequency (TF-IDF) [10] over clinical queries provided my expert clinicians and immunologists.…”

Section: Introductionmentioning

confidence: 99%

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Evidence-Based Recommender System for a COVID-19 Publication Analytics Service

et al. 2021

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The rapid growth of COVID-19 publications has driven clinical researchers and healthcare professionals in pursuit to reduce the knowledge gap on reliable information for effective pandemic solutions. The manual task of retrieving high-quality publications based on the evidence pyramid levels, however, presents a major bottleneck in researchers' workflows. In this paper, we propose an "evidencebased" recommender system namely, KnowCOVID-19 that utilizes an edge computing service to integrate recommender modules for data analytics using end-user thin-clients. The edge computing service features chatbot-based web interface that handles a given COVID-19 publication dataset using two recommender system modules: (i) evidence-based filtering that observes domain specific topics across the literature and classifies the filtered information according to a clinical category, and (ii) social filtering that allows diverse experts with similar objectives to collaborate via a "social plane" to jointly find answers to critical clinical questions to fight the pandemic. We compare the Domain-specific Topic Model (DSTM) used in our evidence-based filtering with state-of-the-art models considering the CORD-19 dataset (a COVID-19 publication archive) and show improved generalization effectiveness as well as knowledge pattern query effectiveness. In addition, we conduct a comparison study between a manual literature review process and the KnowCOVID-19 augmented process, and evaluate the benefits of our information retrieval techniques over important queries provided by COVID-19 clinical experts.

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Section: Related Work a Automation Of Literature Reviewmentioning

confidence: 99%

Section: Expertise Sharing and Crowdsourcingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evidence-Based Recommender System for a COVID-19 Publication Analytics Service

et al. 2021

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“…CORD-19 is a collection of over 400,000 scholarly articles about COVID-19 and related diseases. In our previous work on this dataset, we showed that investigation of the CORD-19 corpus can be simplified through clustering and dimensionality reduction using T-SNE, PCA, and k-Means [7]. The Kaggle notebook from our prior research has attracted great interest in the data science community 2 .…”

Section: Introductionmentioning

confidence: 98%

“…Most of the pre-processing steps presented in Section 3.1 improve upon the data cleaning methods from our prior work on the same dataset [7]. Then, Section 3.2 describes the details of tensor construction and analysis of the latent factors.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 multidimensional kaggle literature organization

Eren¹,

Solovyev²,

Hamer³

et al. 2021

Proceedings of the 21st ACM Symposium on Document Engineering

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The unprecedented outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), or COVID-19, continues to be a significant worldwide problem. As a result, a surge of new COVID-19 related research has followed suit. The growing number of publications requires document organization methods to identify relevant information. In this paper, we expand upon our previous work with clustering the CORD-19 dataset by applying multi-dimensional analysis methods. Tensor factorization is a powerful unsupervised learning method capable of discovering hidden patterns in a document corpus. We show that a higher-order representation of the corpus allows for the simultaneous grouping of similar articles, relevant journals, authors with similar research interests, and topic keywords. These groupings are identified within and among the latent components extracted via tensor decomposition. We further demonstrate the application of this method with a publicly available interactive visualization of the dataset.

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Science gateway adoption using plug‐in middleware for evidence‐based healthcare data management

Oruche

Milman

Alarcon

et al. 2022

Concurrency and Computation

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There is a growing need for next-generation science gateways to increase the accessibility of emerging large-scale datasets for data consumers (e.g., clinicians, researchers) who aim to combat COVID-19-related challenges. Such science gateways that enable access to distributed computing resources for large-scale data management need to be made more programmable, extensible, and scalable. In this article, we propose a novel socio-technical approach for developing a next-generation healthcare science gateway, namely, OnTimeEvidence that addresses data consumer challenges surrounding the COVID-19 pandemic related data analytics. OnTimeEvidence implements an intelligent agent, namely, Vidura Advisor that integrates an evidence-based filtering method to transform manual practices and improve scalability of data analytics. It also features a plug-in management middleware that improves the programmability and extensibility of the science gateway capabilities using microservices. Lastly, we present a usability study that shows the important factors from data consumers' perspective to adopt OnTimeEvidence with chatbot-assisted middleware support to increase their productivity and collaborations to access vast publication archives for rapid knowledge discovery tasks. K E Y W O R D Sdiffusion of innovations, intelligent agents, intelligent middleware, microservices, science gateways INTRODUCTIONManaging the emerging collections of large-scale medical datasets such as scientific publications and electronic health records (EHRs) can be a challenging task for medical data consumers (e.g., clinicians, medical researchers) who need to make timely decisions for combating COVID-19-related issues. Data consumers are constantly faced with complex tasks that are labor-intensive and require domain-specific knowledge discovery over medical information for critical decision making. When synthesizing scientific literature for knowledge discovery, data consumers often rely clinical methodologies such as levels of evidence 1,2 to improve information reliability and reduce the quantity of literature by prioritizing scientific rigor (e.g., expert opinions to systematic reviews and meta-analyses). The challenge of manually filtering high-volume of literature based on evidence-based methods presents the need from data consumers to adopt next-generation science gateways to gain access to emerging large-scale datasets and resources for developing timely pandemic-related solutions.Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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COVID-19 Kaggle Literature Organization

Cited by 10 publications

References 22 publications

Evidence-Based Recommender System for a COVID-19 Publication Analytics Service

Evidence-Based Recommender System for a COVID-19 Publication Analytics Service

COVID-19 multidimensional kaggle literature organization

Science gateway adoption using plug‐in middleware for evidence‐based healthcare data management

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