scholarly journals Gender and Ethnicity Bias in Medicine: A Text Analysis of 1.8 Million Critical Care Records

2022 ◽  
Author(s):  
David Matthew Markowitz

Gender and ethnicity biases are pervasive across many societal domains including politics, employment, and medicine. Such biases will facilitate inequalities until they are revealed and mitigated at scale. To this end, over 1.8 million records from a large US hospital were evaluated with natural language processing techniques in search of gender and ethnicity bias indicators. Consistent with non-linguistic evidence of bias in medicine, physicians often focused on the emotions of female compared to male patients and focused more on the scientific diagnoses of male compared to female patients. Physicians reported on fewer emotions for Black patients versus White patients and physicians demonstrated the greatest need to work through diagnoses for Black women compared to other patients. This work provides evidence of gender and ethnicity biases in medicine as communicated by physicians in the field and requires the critical examination of institutions that perpetuate bias in social systems.

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Patricia Murrieta-Flores ◽  
Ian Gregory

AbstractAlthough the use of Geographic Information Systems (GIS) has a long history in archaeology, spatial technologies have been rarely used to analyse the content of textual collections. A newly developed approach termed Geographic Text Analysis (GTA) is now allowing the semi-automated exploration of large corpora incorporating a combination of Natural Language Processing techniques, Corpus Linguistics, and GIS. In this article we explain the development of GTA, propose possible uses of this methodology in the field of archaeology, and give a summary of the challenges that emerge from this type of analysis.


Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 204
Author(s):  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
X. Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh

A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.


Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 664
Author(s):  
Nikos Kanakaris ◽  
Nikolaos Giarelis ◽  
Ilias Siachos ◽  
Nikos Karacapilidis

We consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph. To the best of our knowledge, this is the first work on the prediction of future research collaborations that combines structural and textual information of a scientific knowledge graph through a purposeful integration of graph algorithms and natural language processing techniques. Our work: (i) investigates whether the integration of unstructured textual data into a single knowledge graph affects the performance of a link prediction model, (ii) studies the effect of previously proposed graph kernels based approaches on the performance of an ML model, as far as the link prediction problem is concerned, and (iii) proposes a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings. We benchmark the proposed approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Finally, we empirically test our approach through various feature combinations with respect to the link prediction problem. Our experimentations with the new COVID-19 Open Research Dataset demonstrate a significant improvement of the abovementioned performance metrics in the prediction of future research collaborations.


AERA Open ◽  
2021 ◽  
Vol 7 ◽  
pp. 233285842110286
Author(s):  
Kylie L. Anglin ◽  
Vivian C. Wong ◽  
Arielle Boguslav

Though there is widespread recognition of the importance of implementation research, evaluators often face intense logistical, budgetary, and methodological challenges in their efforts to assess intervention implementation in the field. This article proposes a set of natural language processing techniques called semantic similarity as an innovative and scalable method of measuring implementation constructs. Semantic similarity methods are an automated approach to quantifying the similarity between texts. By applying semantic similarity to transcripts of intervention sessions, researchers can use the method to determine whether an intervention was delivered with adherence to a structured protocol, and the extent to which an intervention was replicated with consistency across sessions, sites, and studies. This article provides an overview of semantic similarity methods, describes their application within the context of educational evaluations, and provides a proof of concept using an experimental study of the impact of a standardized teacher coaching intervention.


2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Tom Bartlett

AbstractThis paper opens with a problematisation of the notion of real-time in discourse analysis – dissected, as it is, as if time unfolded in a linear and regular procession at the speed of speech. To illustrate this point, the author combines Hasan’s concept of “relevant context” with Bakhtin’s notion of the chronotope to provide an analysis of Sorley MacLean’s poem Hallaig, with its deep-rootedness in space and its dissolution of time. The remainder of the paper is dedicated to following the poem’s metamorphoses and trajectory as it intertwines with Bartlett’s own life and family history, creating a layered simultaneity of meanings orienting to multiple semio-historic centres. In this way the author (pers. comm.) “sets out to illustrate in theory, text analysis and (self-)history the trajectories taken by texts as they cross through time and space; their interconnectedness with social systems at different scales; and the manner in which they are revoiced in order to enhance their legitimacy before the diverse audiences they encounter on their migratory paths.” In this process, Bartlett relates his own story to the socioeconomic concerns of the Hebridean island where his father was raised, and to dialogues between local communities and national and external policy-makers – so echoing Denzin’s call (2014. Interpretive Autoethnography (2nd Edition). Los Angeles: Sage: vii) to “develop a methodology that allows us examine how the private troubles of individuals are connected to public issues and to public responses to these troubles”. Bartlett presents his data through a range of legitimation strategies and voicing techniques, creating transgressive texts that question received notions of identity, authorship, legitimacy and authenticity in academia, the portals of power, and the routines of daily life. The current Abstract is one such example. As with the author’s closing caveat on the potential dangers of self-revelation, offered, no doubt, as a flimsy justification for the extensive focus in the paper on his own life as a chronotope, I leave it for the individual reader to decide if Bartlett’s approach is ultimately ludic or simply ludicrous.


2021 ◽  
pp. 089443932110272
Author(s):  
Qinghong Yang ◽  
Zehong Shi ◽  
Yan Quan Liu

Are core competency requirements for relevant positions in the library shifting? Applying natural language processing techniques to understand the current market demand for core competencies, this study explores job advertisements issued by the American Library Association (ALA) from 2006 to 2017. Research reveals that the job demand continues to rise at a rate of 13% (2006–2017) and that the requirements for work experience are substantially extended, diversity of job titles becomes prevalent, and rich service experience and continuous lifelong learning skills are becoming more and more predominant for librarians. This analytical investigation informs the emerging demands in the American job market debriefing the prioritization and reprioritization of the current core competency requirements for ALA librarians.


2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Siyuan Zhao ◽  
Zhiwei Xu ◽  
Limin Liu ◽  
Mengjie Guo ◽  
Jing Yun

Convolutional neural network (CNN) has revolutionized the field of natural language processing, which is considerably efficient at semantics analysis that underlies difficult natural language processing problems in a variety of domains. The deceptive opinion detection is an important application of the existing CNN models. The detection mechanism based on CNN models has better self-adaptability and can effectively identify all kinds of deceptive opinions. Online opinions are quite short, varying in their types and content. In order to effectively identify deceptive opinions, we need to comprehensively study the characteristics of deceptive opinions and explore novel characteristics besides the textual semantics and emotional polarity that have been widely used in text analysis. In this paper, we optimize the convolutional neural network model by embedding the word order characteristics in its convolution layer and pooling layer, which makes convolutional neural network more suitable for short text classification and deceptive opinions detection. The TensorFlow-based experiments demonstrate that the proposed detection mechanism achieves more accurate deceptive opinion detection results.


1998 ◽  
Vol 4 (1) ◽  
pp. 73-95 ◽  
Author(s):  
KATHLEEN F. MCCOY ◽  
CHRISTOPHER A. PENNINGTON ◽  
ARLENE LUBEROFF BADMAN

Augmentative and Alternative Communication (AAC) is the field of study concerned with providing devices and techniques to augment the communicative ability of a person whose disability makes it difficult to speak or otherwise communicate in an understandable fashion. For several years, we have been applying natural language processing techniques to the field of AAC to develop intelligent communication aids that attempt to provide linguistically correct output while increasing communication rate. Previous effort has resulted in a research prototype called Compansion that expands telegraphic input. In this paper we describe that research prototype and introduce the Intelligent Parser Generator (IPG). IPG is intended to be a practical embodiment of the research prototype aimed at a group of users who have cognitive impairments that affect their linguistic ability. We describe both the theoretical underpinnings of Compansion and the practical considerations in developing a usable system for this population of users.


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