Understanding big data themes from scientific biomedical literature through topic modeling

Altena, Allard J. van; Moerland, Perry D.; Zwinderman, Aeilko H.; Olabarriaga, Sílvia D.

doi:10.1186/s40537-016-0057-0

Cited by 33 publications

(27 citation statements)

References 27 publications

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“…Men thinking conferences are a free pass" (Topic 31, Table 1). This topic model has been used for different applications such as health (Shaw & Karami, 2017;Zhu, Kim, Banerjee, Deferio, Alexopoulos, & Pathak, 2018;Karami, Dahl, Turner--McGrievy, Kharrazi, & Shaw, 2018;Karami & Shaw, 2019), e--petition (Hagen, 2018), politics (Park, Chung, & Park, 2019;Karami, Bennett, & He, 2018;Karami & Elkouri, 2019), opinion mining (Ma, Zhang, Liu, Li, & Yuan, 2016), disaster management (Karami Shah, Vaezi, & Bansal, 2019) business (Amado, Cortez, Rita, & Moro, 2018;Karami & Pendergraft, 2018), social media analysis , automatic summarization of changes in dynamic text collections (Kar, Nunes & Ribeiro, 2015), spam detection (Karami & Zhou, 2014), and systematic literature review (Wang, Ding, Zhao, Huang, Perkins, Zou, & Chen, 2016;Altena, Moerland, Zwinderman, & Olabarriaga, 2016;Shin et al, 2019). We utilized LDA in this research to achieve a deeper semantic layer in the academic sexual harassment corpus.…”

Section: Text Miningmentioning

confidence: 99%

Unwanted advances in higher education:Uncovering sexual harassment experiences in academia with text mining

Karami¹,

White

Ford

et al. 2020

Information Processing & Management

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Sexual harassment in academia is often a hidden problem because victims are usually reluctant to report their experiences. Recently, a web survey was developed to provide an opportunity to share thousands of sexual harassment experiences in academia. Using an efficient approach, this study collected and investigated more than 2,000 sexual harassment experiences to better understand these unwanted advances in higher education. This paper utilized text mining to disclose hidden topics and explore their weight across three variables: harasser gender, institution type, and victim's field of study. We mapped the topics on five themes drawn from the sexual harassment literature and found that more than 50% of the topics were assigned to the unwanted sexual attention theme. Fourteen percent of the topics were in the gender harassment theme, in which insulting, sexist, or degrading comments or behavior was directed towards women. Five percent of the topics involved sexual coercion (a benefit is offered in exchange for sexual favors), 5% involved sex discrimination, and 7% of the topics discussed retaliation against the victim for reporting the harassment, or for simply not complying with the harasser. Findings highlight the power differential between faculty and students, and the toll on students when professors abuse their power. While some topics did differ based on type of institution, there were no differences between the topics based on gender of harasser or field of study. This research can be beneficial to researchers in further investigation of this paper's dataset, and to policymakers in improving existing policies to create a safe and supportive environment in academia.

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Section: Text Miningmentioning

confidence: 99%

Unwanted advances in higher education:Uncovering sexual harassment experiences in academia with text mining

Karami¹,

White

Ford

et al. 2020

Information Processing & Management

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“…Much closer to our work is Altena et al's study on understanding the term big data from a text analysis of bio-medical literature [3]. While there are similarities in the literature corpus and techniques being applied, Altena et al's work differs from this study in that they restrict their study to big data literature in the biomedical field while we analyze all areas of bioinformatics literature.…”

Section: Related Workmentioning

confidence: 87%

“…Several studies have developed approaches to determine the optimal number of topics [4,22]. While there are likelihood based measures that help determine the right number of topics, these measures cannot be used alone to find the best model [3].…”

Section: Introductionmentioning

confidence: 99%

What’s Hot and What’s Not? - Exploring Trends in Bioinformatics Literature Using Topic Modeling and Keyword Analysis

Hahn¹,

Mohanty²,

Manda³

2017

Lecture Notes in Computer Science

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Abstract. Scientists exploring a new area of research are interested to know the "hot" topics in that area in order to make informed choices. With exponential growth in scientific literature, identifying such trends manually is not easy. Topic modeling has emerged as an effective approach to analyze large volumes of text. While this approach has been applied on literature in other scientific areas, there has been no formal analysis of bioinformatics literature.Here, we conduct keyword and topic model-based analysis on bioinformatics literature starting from 1998 to 2016. We identify top keywords and topics per year and explore temporal popularity trends of those keywords/areas. Network analysis was conducted to identify clusters of sub-areas/topics in bioinformatics. We found that "big-data", "next generation sequencing", and "cancer" all experienced exponential increase in popularity over the years. On the other hand, interest in drug discovery has plateaued after the early 2000s.

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“…Frequency analysis provides overall information about the detected DsSs [18]. Exploring highfrequency DsSs with bar chart and word cloud is a starting point for content analysis.…”

Section: Frequency Analysismentioning

confidence: 99%

“…LDA has been utilized for health applications such as diet, diabetes, exercise, and obesity [28,29,30], and LGBT health issues [31,32], and non-health applications such as business and organizations [33,34,35], spam detection [36], disaster management [37], and politics [38,39]. There are some work investigating related studies in medical and health domains using LDA such as exploring the literature of depressive disorders [17], biomedical literature [40,18], and adolescent substance use and depression [41]. Figure 3: An Example of LDA [42] To the best of our knowledge, this study is the first research uses LDA to analyze medical case reports.…”

Section: Relationship Detection and Analysismentioning

confidence: 99%

Exploring diseases and syndromes in neurology case reports from 1955 to 2017 with text mining

Karami

Ghasemi

Sen

et al. 2019

Computers in Biology and Medicine

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Background: A large number of neurology case reports have been published, but it is a challenging task for human medical experts to explore all of these publications. Text mining offers a computational approach to investigate neurology literature and capture meaningful patterns. The overarching goal of this study is to provide a new perspective on case reports of neurological disease and syndrome analysis over the last six decades using text mining.Methods: We extracted diseases and syndromes (DsSs) from more than 65,000 neurology case reports from 66 journals in PubMed over the last six decades from 1955 to 2017. Text mining was applied to reports on the detected DsSs to investigate high-frequency DsSs, categorize them, and explore the linear trends over the 63-year time frame.Results: The text mining methods explored high-frequency neurologic DsSs and their trends and the relationships between them from 1955 to 2017. We detected more than 18,000 unique DsSs and found 10 categories of neurologic DsSs. While the trend analysis showed the increasing trends in the case reports for top-10 high-frequency DsSs, the categories had mixed trends.Conclusion: Our study provided new insights into the application of text mining methods to investigate DsSs in a large number of medical case reports that occur over several decades. The proposed approach can be used to provide a macro level analysis of medical literature by discovering interesting patterns and tracking them over several years to help physicians explore these case reports more efficiently.reports that contain single or multiple cases. Medical case reports (MCRs) usually represent rare cases that are the only evidence before published trials [1]. MCRs are "a formal summary of a unique patient and his or her illness, including the presenting signs and symptoms, diagnostic studies, treatment course and outcome" [2], and are sometimes the first reportage of new discoveries [3]. However, there is no standard format for MCRs and it is difficult to get a macro level perspective [3]. Moreover, MCRs have been historically considered as a cost-and time-effective method in advancing knowledge and education from both patient and physician perspectives with direct impact on clinical practice and patient care [4]. They have several benefits including the presentation of new applications, discussion of new diseases and syndromes (DsSs), generation of hypotheses and novel solutions, and providing educational value to the medical professions [3]. In the late 1990s, interest in MCRs grew. For example, the Lancet introduced a peer-reviewed "Case Reports" section and some new journals were launched for the sole purpose of publishing MCRs [3].The increasing number of published neurology case reports makes the macro level analysis almost impossible because it is a challenge for researchers to keep up-to-date with published medical case reports. Moreover, the number of case series is far fewer than the number of single case reports [5]. This issue requires a new approach to review neur...

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Understanding big data themes from scientific biomedical literature through topic modeling

Cited by 33 publications

References 27 publications

Unwanted advances in higher education:Uncovering sexual harassment experiences in academia with text mining

Unwanted advances in higher education:Uncovering sexual harassment experiences in academia with text mining

What’s Hot and What’s Not? - Exploring Trends in Bioinformatics Literature Using Topic Modeling and Keyword Analysis

Exploring diseases and syndromes in neurology case reports from 1955 to 2017 with text mining

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