Extending <scp>SemRep</scp> to the public health domain

Rosemblat, Graciela; Resnick, Melissa; Auston, Ione; Shin, Dongwook; Sneiderman, Charles; Fizsman, Marcelo; Rindflesch, Thomas C.

doi:10.1002/asi.22899

Cited by 10 publications

(11 citation statements)

References 22 publications

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“…As shown in Table 3 , blogs follow Twitter with a Spearman correlation of 0.124 with citations. Citations show a positive correlation with the number of references (0.416), the collaboration indicators (particularly the number of authors: 0.281), and paper length (0.194); these findings have been documented in many other bibliometric studies (e.g., [ 41 – 47 ]).…”

Section: Resultssupporting

confidence: 59%

“…5 provides clear evidence that the relationship between the number of authors and citation and social media metrics—especially Twitter—is positive. The relationship between citations and number of authors has been well documented before [ 42 , 52 – 54 ], and is not caused by authors’ self-citations [ 47 ]. However, the effect of “self-mentions” in social media on these trends has yet to be assessed.…”

Section: Resultsmentioning

confidence: 88%

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Characterizing Social Media Metrics of Scholarly Papers: The Effect of Document Properties and Collaboration Patterns

2015

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A number of new metrics based on social media platforms—grouped under the term “altmetrics”—have recently been introduced as potential indicators of research impact. Despite their current popularity, there is a lack of information regarding the determinants of these metrics. Using publication and citation data from 1.3 million papers published in 2012 and covered in Thomson Reuters’ Web of Science as well as social media counts from Altmetric.com, this paper analyses the main patterns of five social media metrics as a function of document characteristics (i.e., discipline, document type, title length, number of pages and references) and collaborative practices and compares them to patterns known for citations. Results show that the presence of papers on social media is low, with 21.5% of papers receiving at least one tweet, 4.7% being shared on Facebook, 1.9% mentioned on blogs, 0.8% found on Google+ and 0.7% discussed in mainstream media. By contrast, 66.8% of papers have received at least one citation. Our findings show that both citations and social media metrics increase with the extent of collaboration and the length of the references list. On the other hand, while editorials and news items are seldom cited, it is these types of document that are the most popular on Twitter. Similarly, while longer papers typically attract more citations, an opposite trend is seen on social media platforms. Finally, contrary to what is observed for citations, it is papers in the Social Sciences and humanities that are the most often found on social media platforms. On the whole, these findings suggest that factors driving social media and citations are different. Therefore, social media metrics cannot actually be seen as alternatives to citations; at most, they may function as complements to other type of indicators.

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Section: Resultssupporting

confidence: 59%

Section: Resultsmentioning

confidence: 88%

Characterizing Social Media Metrics of Scholarly Papers: The Effect of Document Properties and Collaboration Patterns

2015

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“… 24 SemRep extracts three-part propositions from biomedical text sentences in MEDLINE format, called semantic predicates. 25 The subject and object of each semantic predicate are derived from the UMLS metathesaurus. 26 The relationship between them is the mutual relation structure provided by UMLS semantic network through 135 semantic types.…”

Section: Methodsmentioning

confidence: 99%

<p>Integrating Unified Medical Language System and Kleinberg’s Burst Detection Algorithm into Research Topics of Medications for Post-Traumatic Stress Disorder</p>

Xu¹,

Xu²,

Wen³

et al. 2020

DDDT

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Background: The treatment of post-traumatic stress disorder (PTSD) has long been a challenge because the symptoms of PTSD are multifaceted. PTSD is primarily treated with psychotherapy and medication, or a combination of psychotherapy and medication. The present study was designed to analyze the literature on medications for PTSD and explore high-frequency common drugs and low-frequency burst drugs by burst detection algorithm combined with Unified Medical Language System (UMLS) and provide references for developing new drugs for PTSD. Methods: Publications related to medications for PTSD from 2010 to 2019 were identified through PubMed, Web of Science Core Collection, and BIOSIS Previews. SemRep and SemRep semantic result processing system were performed to extract the set of drug concepts with therapeutic relationship according to the semantic relationship of UMLS. Kleinberg's burst detection algorithm was applied to calculate the burst weight index of drug concepts by a Java-based program. These concepts were sorted according to the frequency and the burst weight index. Results: Four hundred and fifty-nine treatment-related drug concepts were extracted. The drug with the highest burst weight index was "Psilocybine", a hallucinogen, which was more likely to be a hotspot for the pharmacotherapy of PTSD. The highest frequency concept was "prazosin", which was more likely to be the focus of research in the medications for PTSD. Conclusion: The present study assessed the medication-related literature on PTSD treatment, providing a framework of burst words detection-based method, a baseline of information for future research and the new attempt for the discovery of textual knowledge. The bibliometric analysis based on the burst detection algorithm combined with UMLS has shown certain feasibility in amplifying the microscopic changes of a specific research direction in a field, it can also be used in other aspects of disease and to explore the trends of various disciplines.

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“…Rosemblat, et al aimed to build an ontology in public health promotion to support the extension of SemRep [28]. Our approach uses an open information extraction NLP tool, which gave us the flexibility to develop a customized ontology representing the conceptualization of the specific domain.…”

Section: Introductionmentioning

confidence: 99%

Lightweight predicate extraction for patient-level cancer information and ontology development

Amith

Song

Zhang

et al. 2017

BMC Med Inform Decis Mak

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BackgroundKnowledge engineering for ontological knowledgebases is resource and time intensive. To alleviate these issues, especially for novices, automated tools from the natural language domain can assist in the development process of ontologies. We focus towards the development of ontologies for the public health domain and use patient-centric sources from MedlinePlus related to HPV-causing cancers.MethodsThis paper demonstrates the use of a lightweight open information extraction (OIE) tool to derive accurate knowledge triples that can lead to the seeding of an ontological knowledgebase. We developed a custom application, which interfaced with an information extraction software library, to help facilitate the tasks towards producing knowledge triples from textual sources.ResultsThe results of our efforts generated accurate extractions ranging from 80–89% precision. These triples can later be transformed to OWL/RDF representation for our planned ontological knowledgebase.ConclusionsOIE delivers an effective and accessible method towards the development ontologies.

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Extending SemRep to the public health domain

Cited by 10 publications

References 22 publications

Characterizing Social Media Metrics of Scholarly Papers: The Effect of Document Properties and Collaboration Patterns

Characterizing Social Media Metrics of Scholarly Papers: The Effect of Document Properties and Collaboration Patterns

<p>Integrating Unified Medical Language System and Kleinberg’s Burst Detection Algorithm into Research Topics of Medications for Post-Traumatic Stress Disorder</p>

Lightweight predicate extraction for patient-level cancer information and ontology development

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