Trends and Features of the Applications of Natural Language Processing Techniques for Clinical Trials Text Analysis

Chen, Xieling; Xie, Haoran; Cheng, Gary; Poon, Leonard K. M.; Leng, Mingming; Wang, Fu Lee

doi:10.3390/app10062157

Cited by 43 publications

(18 citation statements)

References 120 publications

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“…Such exploratory summaries describe the data succinctly, provide a better understanding of the data, and helps generate insights which inform subsequent classification analysis. Past studies explored custom approaches to identifying constructs such as dominance behavior in electronic chat, indicating the tremendous potential for extending such analyses by using machine learning techniques to accelerate automated sentiment classification and the subsections that follow present key insights gained from literature review to support and inform the textual analytics processes used in this study [14][15][16][17].…”

Section: Literature Reviewmentioning

confidence: 99%

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

et al. 2020

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Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19’s informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naïve Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

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Section: Literature Reviewmentioning

confidence: 99%

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

et al. 2020

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“…If we focus on medical texts, we see that modern NLP methods have been applied to clinical decision support, e.g., [13], to clinical trials [14], to automatic extraction of adverse drug events and drug related entities [15], and to other areas [16,17].…”

Section: Five Decades Of Automated Analysis Of Medical Textsmentioning

confidence: 99%

Identifying Condition-action Statements in Medical Guidelines: Three Studies using Machine Learning and Domain Adaptation

Hematialam

Zadrozny

2021

Preprint

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Background: Medical guidelines provide the conceptual link between a diagnosis and a recommendation. They often disagree on their recommendations. There are over thirty five thousand guidelines indexed by PubMed, which creates a need for automated methods for analysis of recommendations, i.e., recommended actions, for similar conditions. Results: This article advances the state of the art in text understanding of medical guidelines by showing the applicability of transformer-based models and transfer learning (domain adaptation) to the problem of finding condition-action and other conditional sentences. We report results of three studies using syntactic, semantic and deep learning methods, with and without transformer-based models such as BioBERT and BERT. We perform in depth evaluation on a set of three annotated medical guidelines. Our experiments show that a combination of machine learning domain adaptation and transfer can improve the ability to automatically find conditional sentences in clinical guidelines. We show substantial improvements over prior art (up to 25%), and discuss several directions of extending this work, including addressing the problem of paucity of annotated data.Conclusion: Modern deep learning methods, when applied to the text of clinical guidelines, yield substantial improvements in our ability to find sentences expressing the relations of condition-consequence, condition-action and action.

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“…Such exploratory summaries describe the data succinctly, provide a better understanding of the data, and helps generate insights which inform subsequent classification analysis. Past studies have explored custom approaches to identifying constructs such as dominance behavior in electronic chat, indicating the tremendous potential for extending such analyses by using machine learning techniques to accelerate automated sentiment classification and the subsections that follow present key insights gained from literature review to support and inform the Textual Analytics processes used in this study [15][16][17][18][19].…”

Section: Literature Reviewmentioning

confidence: 99%

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

Samuel

Ali

Rahman

et al. 2020

Preprint

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Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19's informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91\% for short Tweets, with the Na\"ive Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74\% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

show abstract

Trends and Features of the Applications of Natural Language Processing Techniques for Clinical Trials Text Analysis

Cited by 43 publications

References 120 publications

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

Identifying Condition-action Statements in Medical Guidelines: Three Studies using Machine Learning and Domain Adaptation

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

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