Identifying Medications that Patients Stopped Taking in Online Health Forums

Cho, Jason H. D.; Gao, Tony; Gîrju, Roxana

doi:10.1109/icsc.2017.24

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…To our knowledge, this is the first study to use a machine learning approach to detect the smoking status of users as “quit vs not” from individual posts in an online smoking cessation community. Compared to previous studies that identified individual health status by mining UGCs [25,26], this work further highlights the value of combining insights from domain experts, especially in the extraction of domain-specific features, and computational methods. Our approach is also the first to examine “neighboring posts” to leverage semantic connections between UGCs.…”

Section: Discussionmentioning

confidence: 76%

“…In particular, the proliferation of social networks and online communities represents exciting opportunities for large-scale data analysis and intervention design. Researchers have leveraged data from these online platforms to study user engagement [21,22], predict population-level health status (e.g., influenza) [23,24], and analyze individuals’ offline health status (e.g., depression and drug usage) [25,26].…”

Section: Introductionmentioning

confidence: 99%

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Mining user-generated content in an online smoking cessation community to identify smoking status: A machine learning approach

Wang

Zhao

Cha

et al. 2019

Decision Support Systems

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Online smoking cessation communities help hundreds of thousands of smokers quit smoking and stay abstinent each year. Content shared by users of such communities may contain important information that could enable more effective and personally tailored cessation treatment recommendations. This study demonstrates a novel approach to determine individuals’ smoking status by applying machine learning techniques to classify user-generated content in an online cessation community. Study data were from BecomeAnEX.org, a large, online smoking cessation community. We extracted three types of novel features from a post: domain-specific features, author-based features, and thread-based features. These features helped to improve the smoking status identification (quit vs. not) performance by 9.7% compared to using only text features of a post’s content. In other words, knowledge from domain experts, data regarding the post author’s patterns of online engagement, and other community member reactions to the post can help to determine the focal post author’s smoking status, over and above the actual content of a focal post. We demonstrated that machine learning methods can be applied to user-generated data from online cessation communities to validly and reliably discern important user characteristics, which could aid decision support on intervention tailoring.

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Section: Discussionmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Mining user-generated content in an online smoking cessation community to identify smoking status: A machine learning approach

Wang

Zhao

Cha

et al. 2019

Decision Support Systems

View full text Add to dashboard Cite

show abstract

“…Patients are willing to share highly personal information on social media, including thoughts, feelings, and behaviors that they typically would not disclose because of stigma or embarrassment [ 11 , 12 ]. Scientists have begun to search these data for important psychological clues about patient behaviors and outcomes, such as medication adherence and adverse reactions to medications [ 13 , 14 ]. By combining psychological insights with data science methods such as machine learning, it is possible to develop mathematical models that mine social media data to predict people’s health behaviors at a population and individual patient level.…”

Section: Introductionmentioning

confidence: 99%

Social Media as a New Vital Sign: Commentary

Young¹

2018

J Med Internet Res

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Mobile technologies, such as wireless glucometers and mobile health apps, are increasingly being integrated into health and medical care. Because patients openly share real-time information about their health behaviors and outcomes on social media, social media data may also be used as a tool for monitoring patient care. This commentary describes how recent advances in computer science, psychology, and medicine enable social media data to become a new health “vital sign,” as well as actionable steps that public health officials, health systems, and clinics can take to integrate social data into both public and population health as well as into individual patient care. Barriers that first need to be addressed, including privacy concerns, legal and ethical responsibilities, and infrastructure support, are discussed.

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Social Monitoring for Public Health

Paul¹,

Dredze²,

Marchionini³

2017

Synthesis Lectures on Information Concepts, Retrieval, and Services

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Public health thrives on high-quality evidence, yet acquiring meaningful data on a population remains a central challenge of public health research and practice. Social monitoring, the analysis of social media and other user-generated web data, has brought advances in the way we leverage population data to understand health. Social media offers advantages over traditional data sources, including real-time data availability, ease of access, and reduced cost. Social media allows us to ask, and answer, questions we never thought possible.This book presents an overview of the progress on uses of social monitoring to study public health over the past decade. We explain available data sources, common methods, and survey research on social monitoring in a wide range of public health areas. Our examples come from topics such as disease surveillance, behavioral medicine, and mental health, among others. We explore the limitations and concerns of these methods. Our survey of this exciting new field of data-driven research lays out future research directions.

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Identifying Medications that Patients Stopped Taking in Online Health Forums

Cited by 5 publications

References 20 publications

Mining user-generated content in an online smoking cessation community to identify smoking status: A machine learning approach

Mining user-generated content in an online smoking cessation community to identify smoking status: A machine learning approach

Social Media as a New Vital Sign: Commentary

Social Monitoring for Public Health

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