A topic modeling approach reveals the dynamic T cell composition of peripheral blood during cancer immunotherapy.
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ABSTRACT:
SUBMITTER: Peng X
PROVIDER: S-EPMC10475788 | biostudies-literature | 2023 Aug
REPOSITORIES: biostudies-literature
Publications
A topic modeling approach reveals the dynamic T cell composition of peripheral blood during cancer immunotherapy.
Peng Xiyu X Lee Jasme J Adamow Matthew M Maher Colleen C Postow Michael A MA Callahan Margaret K MK Panageas Katherine S KS Shen Ronglai R
Cell reports methods 20230802 8
We present TopicFlow, a computational framework for flow cytometry data analysis of patient blood samples for the identification of functional and dynamic topics in circulating T cell population. This framework applies a Latent Dirichlet Allocation (LDA) model, adapting the concept of topic modeling in text mining to flow cytometry. To demonstrate the utility of our method, we conducted an analysis of ∼17 million T cells collected from 138 peripheral blood samples in 51 patients with melanoma un ...[more]
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Project description:BackgroundIn December 2019, a few coronavirus disease (COVID-19) cases were first reported in Wuhan, Hubei, China. Soon after, increasing numbers of cases were detected in other parts of China, eventually leading to a disease outbreak in China. As this dreadful disease spreads rapidly, the mass media has been active in community education on COVID-19 by delivering health information about this novel coronavirus, such as its pathogenesis, spread, prevention, and containment.ObjectiveThe aim of this study was to collect media reports on COVID-19 and investigate the patterns of media-directed health communications as well as the role of the media in this ongoing COVID-19 crisis in China.MethodsWe adopted the WiseSearch database to extract related news articles about the coronavirus from major press media between January 1, 2020, and February 20, 2020. We then sorted and analyzed the data using Python software and Python package Jieba. We sought a suitable topic number with evidence of the coherence number. We operated latent Dirichlet allocation topic modeling with a suitable topic number and generated corresponding keywords and topic names. We then divided these topics into different themes by plotting them into a 2D plane via multidimensional scaling.ResultsAfter removing duplications and irrelevant reports, our search identified 7791 relevant news reports. We listed the number of articles published per day. According to the coherence value, we chose 20 as the number of topics and generated the topics' themes and keywords. These topics were categorized into nine main primary themes based on the topic visualization figure. The top three most popular themes were prevention and control procedures, medical treatment and research, and global or local social and economic influences, accounting for 32.57% (n=2538), 16.08% (n=1258), and 11.79% (n=919) of the collected reports, respectively.ConclusionsTopic modeling of news articles can produce useful information about the significance of mass media for early health communication. Comparing the number of articles for each day and the outbreak development, we noted that mass media news reports in China lagged behind the development of COVID-19. The major themes accounted for around half the content and tended to focus on the larger society rather than on individuals. The COVID-19 crisis has become a worldwide issue, and society has become concerned about donations and support as well as mental health among others. We recommend that future work addresses the mass media's actual impact on readers during the COVID-19 crisis through sentiment analysis of news data.
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Project description:BackgroundSocial media platforms allow individuals to openly gather, communicate, and share information about their interactions with health care services, becoming an essential supplemental means of understanding patient experience.ObjectiveWe aimed to identify common discussion topics related to health care experience from the public's perspective and to determine areas of concern from patients' perspectives that health care providers should act on.MethodsThis study conducted a spatiotemporal analysis of the volume, sentiment, and topic of patient experience-related posts on the Weibo platform developed by Sina Corporation. We applied a supervised machine learning approach including human annotation and machine learning-based models for topic modeling and sentiment analysis of the public discourse. A multiclassifier voting method based on logistic regression, multinomial naïve Bayes, and random forest was used.ResultsA total of 4008 posts were manually classified into patient experience topics. A patient experience theme framework was developed. The accuracy, precision, recall, and F-measure of the method integrating logistic regression, multinomial naïve Bayes, and random forest for patient experience themes were 0.93, 0.95, 0.80, 0.77, and 0.84, respectively, indicating a satisfactory prediction. The sentiment analysis revealed that negative sentiment posts constituted the highest proportion (3319/4008, 82.81%). Twenty patient experience themes were discussed on the social media platform. The majority of the posts described the interpersonal aspects of care (2947/4008, 73.53%); the five most frequently discussed topics were "health care professionals' attitude," "access to care," "communication, information, and education," "technical competence," and "efficacy of treatment."ConclusionsHospital administrators and clinicians should consider the value of social media and pay attention to what patients and their family members are communicating on social media. To increase the utility of these data, a machine learning algorithm can be used for topic modeling. The results of this study highlighted the interpersonal and functional aspects of care, especially the interpersonal aspects, which are often the "moment of truth" during a service encounter in which patients make a critical evaluation of hospital services.
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