Detecting clinically related content in online patient posts

VanDam, Courtland; Kanthawala, Shaheen; Pratt, Wanda; Chai, Joyce; Huh, Jina

doi:10.1016/j.jbi.2017.09.015

Cited by 19 publications

(7 citation statements)

References 28 publications

(34 reference statements)

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“…Chakravorti and colleagues [12] extracted topics based on various health issues discussed in online forums by evaluating user posts of several subreddits (e.g., r/Depression, r/Anxiety) from 2012 to 2018. VanDam and colleagues [13] presented a classification approach for identifying clinic-related posts in online health communities. For that dataset, the authors collected 9576 threadinitiating posts from WebMD, which is a health information website.…”

Section: Related Workmentioning

confidence: 99%

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

Jelodar

Wang

Orji

et al. 2020

IEEE J. Biomed. Health Inform.

303

166

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Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19-related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making. In addition, experiments demonstrated that the research model achieved an accuracy of 81.15%-a higher accuracy than that of several other well-known machine-learning algorithms for COVID-19-Sentiment Classification.

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Section: Related Workmentioning

confidence: 99%

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

Jelodar

Wang

Orji

et al. 2020

IEEE J. Biomed. Health Inform.

303

166

View full text Add to dashboard Cite

show abstract

“…By grouping our study populations in this way, we are not suggesting that online research is only suitable in these extreme cases. Online research can also be appropriate for more general identification of online peer support groups (Suzuki & Calzo, 2004) as well as researching specific health conditions (VanDam, Kanthawala, Pratt, Chai, & Huh, 2017;Zhang et al, 2016 • Online research presents opportunities for health researchers to access populations, opinions, and experiences which can be challenging to capture offline.…”

Section: Methodsmentioning

confidence: 99%

The Ethical Implications of Collecting Data From Online Health Communities

Harris¹,

Germain²,

Maxwell³

et al. 2020

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Online communities can provide researchers with a raft of knowledge about a range of populations and groups. While ethical considerations in online research are complicated and nuanced, they are often underreported in existing research. Issues concerning the use of verbatim quotes from online communities, seeking consent, and protecting from harm and distinctions between private and public spaces in particular have generated much debate in recent years. We advocate for greater thought to be given to the ethical implications of online research and reflect on what should be considered public information, the protection of anonymity, and how to protect online users from harm. This case study provides an insight into the practicalities of conducting online research using examples from research conducted by four doctoral students.

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“…In (6), an online_post provides not only narrative text but also other important information, such as background, label, topic, caption, and post attributes [ 22 ]. Here, we extract information for the analysis of posts by using novel knowledge-involved techniques.…”

Section: Methodsmentioning

confidence: 99%

Knowledge Discovery from Posts in Online Health Communities Using Unified Medical Language System

Chen

Zhang

Liu

et al. 2018

IJERPH

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Patient-reported posts in Online Health Communities (OHCs) contain various valuable information that can help establish knowledge-based online support for online patients. However, utilizing these reports to improve online patient services in the absence of appropriate medical and healthcare expert knowledge is difficult. Thus, we propose a comprehensive knowledge discovery method that is based on the Unified Medical Language System for the analysis of narrative posts in OHCs. First, we propose a domain-knowledge support framework for OHCs to provide a basis for post analysis. Second, we develop a Knowledge-Involved Topic Modeling (KI-TM) method to extract and expand explicit knowledge within the text. We propose four metrics, namely, explicit knowledge rate, latent knowledge rate, knowledge correlation rate, and perplexity, for the evaluation of the KI-TM method. Our experimental results indicate that our proposed method outperforms existing methods in terms of providing knowledge support. Our method enhances knowledge support for online patients and can help develop intelligent OHCs in the future.

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Detecting clinically related content in online patient posts

Cited by 19 publications

References 28 publications

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

The Ethical Implications of Collecting Data From Online Health Communities

Knowledge Discovery from Posts in Online Health Communities Using Unified Medical Language System

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