Detection and Analysis of Drug Misuses. A Study Based on Social Media Messages

Bigeard, Elise; Grabar, Natalia; Thiessard, Frantz

doi:10.3389/fphar.2018.00791

Cited by 16 publications

(18 citation statements)

References 25 publications

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“…Pharmacovigilance -i.e. the post-market surveillance of drugs -was an early health-related focus for social media NLP [92,93] and has remained an important subject of research, with applications including the identification of mentions of Adverse Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and Consumer-Generated Data Drug Reactions (ADRs) [51,55]. One recent study focussed on topics related to Thyroid Hormone Replacement Therapy (THRT), particularly on the identification of side effects [50].…”

Section: Pharmacovigilancementioning

confidence: 99%

Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data

Conway

Chapman

2019

Yearb Med Inform

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Objective: We present a narrative review of recent work on the utilisation of Natural Language Processing (NLP) for the analysis of social media (including online health communities) specifically for public health applications. Methods: We conducted a literature review of NLP research that utilised social media or online consumer-generated text for public health applications, focussing on the years 2016 to 2018. Papers were identified in several ways, including PubMed searches and the inspection of recent conference proceedings from the Association of Computational Linguistics (ACL), the Conference on Human Factors in Computing Systems (CHI), and the International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Web and Social Media (ICWSM). Popular data sources included Twitter, Reddit, various online health communities, and Facebook. Results: In the recent past, communicable diseases (e.g., influenza, dengue) have been the focus of much social media-based NLP health research. However, mental health and substance use and abuse (including the use of tobacco, alcohol, marijuana, and opioids) have been the subject of an increasing volume of research in the 2016 - 2018 period. Associated with this trend, the use of lexicon-based methods remains popular given the availability of psychologically validated lexical resources suitable for mental health and substance abuse research. Finally, we found that in the period under review “modern" machine learning methods (i.e. deep neural-network-based methods), while increasing in popularity, remain less widely used than “classical" machine learning methods.

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Section: Pharmacovigilancementioning

confidence: 99%

Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data

Conway

Chapman

2019

Yearb Med Inform

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“…radio, TV and newspapers) and some of them who suffer from a chronic disease may become experts on their own illness. This knowledge, however, is not reliable, since, as observed in several studies, "ordinary people" might misunderstand medical information in good faith (Claveau et al, 2015;Bigeard et al, 2018).…”

Section: A Corpus For Layficationmentioning

confidence: 99%

“…using "lack of iron" 12 rather than "anemia") or reformulation (e.g. "Anaemia is a lack of red blood cells" 13 ) can help prevent unwanted consequences such as the misunderstandings (Claveau et al, 2015) that may cause medication misuses (Bigeard et al, 2018). A better understanding of medical jargon is especially important for elderly people affected by chronic diseases because it facilitates a proactive behaviour and fosters self-empowerment, which has proven to be beneficial for long-term successful treatment (Fotokian et al, 2017).…”

Section: A Corpus For Layficationmentioning

confidence: 99%

Designing an Extensible Domain-Specific Web Corpus for “Layfication”

Santini

Jönsson

Strandqvist

et al. 2019

Advances in Systems Analysis, Software Engineering, and High Performance Computing

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In the era of data-driven science, corpus-based language technology is an essential part of cyber physical systems. In this chapter, the authors describe the design and the development of an extensible domain-specific web corpus to be used in a distributed social application for the care of the elderly at home. The domain of interest is the medical field of chronic diseases. The corpus is conceived as a flexible and extensible textual resource, where additional documents and additional languages will be appended over time. The main purpose of the corpus is to be used for building and training language technology applications for the “layfication” of the specialized medical jargon. “Layfication” refers to the automatic identification of more intuitive linguistic expressions that can help laypeople (e.g., patients, family caregivers, and home care aides) understand medical terms, which often appear opaque. Exploratory experiments are presented and discussed.

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“…A number of recent studies have proposed the use of social media for PM and illicit drug abuse monitoring. (11)(12)(13)(14) Data from social media offers a unique opportunity to study human behavior, including behavior associated with the nonmedical use of PMs, at a large scale. It also enables researchers and public health o cials to monitor the trends of nonmedical PM use incidents, improve monitoring strategies, and analyze user behaviors.…”

Section: Introductionmentioning

confidence: 99%

“…(17,18) The task of automatically detecting information about nonmedical PM use, misuse and abuse has been shown to be particularly complex for NLP and machine learning due to factors such as data imbalance (i.e., only a small portion of the chatter associated with a PM represents self-reports of nonmedical use or abuse), low agreements among manual curators/annotators (i.e., humans often nd it di cult to determine if a user post represents nonmedical use or not), and ambiguous contexts (i.e., contextual cues indicate nonmedical use, which are detectable by humans but not traditional machine learning models). (9,11) Consequently, automatic systems, including our past system, for detecting nonmedical PM use from social media have typically shown low performances. (16,19) Therefore, the development of systems that can automatically detect and lter chatter that represent nonmedical PM use is a fundamental necessity for establishing social media based near real-time monitoring.…”

Section: Introductionmentioning

confidence: 99%

Text Classification Models for the Automatic Detection of Nonmedical Prescription Medication Use from Social Media

Al-Garadi

Yang

Cai

et al. 2020

Preprint

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Background Prescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging—requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter. Methods We experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority “abuse/misuse” class. Results Our proposed fusion-based model performs significantly better than the best traditional model (F1-score [95% CI]: 0.67 [0.64–0.69] vs. 0.45 [0.42–0.48]). We illustrate, via experimentation using differing training set sizes, that the transformer-based models are more stable and require less annotated data compared to the other models. The significant improvements achieved by our best-performing classification model over past approaches makes it suitable for automated continuous monitoring of nonmedical PM use from Twitter. Conclusions BERT, BERT-like and fusion-based models not only outperform traditional machine learning and deep learning models, but also show substantial improvements over many years of past research on the topic of prescription medication misuse/abuse classification from social media, which had been shown to be a complex task due to the unique ways in which information about nonmedical use is presented. However, several challenges, such as lack of complete context and the nature of social media language, must be overcome to further improve BERT and BERT-like models despite their advantages over other approaches. These experimental driven challenges are represented as potential future research directions.

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Detection and Analysis of Drug Misuses. A Study Based on Social Media Messages

Cited by 16 publications

References 25 publications

Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data

Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data

Designing an Extensible Domain-Specific Web Corpus for “Layfication”

Text Classification Models for the Automatic Detection of Nonmedical Prescription Medication Use from Social Media

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