A Hybrid Approach for Drug Abuse Events Extraction from Twitter

Jenhani, Ferdaous; Gouider, Mohamed Salah; Saïd, Lamjed Ben

doi:10.1016/j.procs.2016.08.121

Cited by 18 publications

(15 citation statements)

References 9 publications

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“…While the method is simple, its result might present no explicit clinical relevance of a derived drug-event pair [ 9 ] due to disregard relational context that might express an exact impression in a clinical event such as a drug treats a symptom or a drug causes a symptom. To fill in this research gap, many researchers consider surrounding contexts around drug and event entities within clinical texts and represent such data by either using pattern-based method [ 10 – 15 ] or feature-based method [ 16 – 18 ]. Consequently, a potential ADR is identified by either training supervised learning or semisupervised learning [ 19 ] model.…”

Section: Introductionmentioning

confidence: 99%

“…In our previous work [ 13 ], a pattern-based method has been proposed to utilize labels weakly suggested by a set of simple rules, (distant supervision) and pattern distribution has been investigated for characterizing ADR relations. Different from [ 10 – 12 , 18 , 37 ], a pattern-based method is acquired as feature representation and machine learning methods such as support vector machine (SVM), decision tree C4.5 (DT), random forest (RF), or naïve Bayes (NB) are well-established as a classifier. Kang et al [ 36 ] deploy a graph base and applies the shortest-path preference to ADR identification.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records

Taewijit

Theeramunkong

Ikeda

2017

Journal of Healthcare Engineering

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Information extraction and knowledge discovery regarding adverse drug reaction (ADR) from large-scale clinical texts are very useful and needy processes. Two major difficulties of this task are the lack of domain experts for labeling examples and intractable processing of unstructured clinical texts. Even though most previous works have been conducted on these issues by applying semisupervised learning for the former and a word-based approach for the latter, they face with complexity in an acquisition of initial labeled data and ignorance of structured sequence of natural language. In this study, we propose automatic data labeling by distant supervision where knowledge bases are exploited to assign an entity-level relation label for each drugevent pair in texts, and then, we use patterns for characterizing ADR relation. The multiple-instance learning with expectationmaximization method is employed to estimate model parameters. The method applies transductive learning to iteratively reassign a probability of unknown drug-event pair at the training time. By investigating experiments with 50,998 discharge summaries, we evaluate our method by varying large number of parameters, that is, pattern types, pattern-weighting models, and initial and iterative weightings of relations for unlabeled data. Based on evaluations, our proposed method outperforms the word-based feature for NB-EM (iEM), MILR, and TSVM with F1 score of 11.3%, 9.3%, and 6.5% improvement, respectively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records

Taewijit

Theeramunkong

Ikeda

2017

Journal of Healthcare Engineering

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“…Proposed hybrid techniques outperformed by 24.8% for Twitter text. 58 EE from Twitter is an arduous task because of valuable user-generated text in the form of tweets to extract general and specific event information. EE techniques for Twitter can be based on event type, tasks, detection method, and features.…”

Section: Ie For Unstructured Data Analysismentioning

confidence: 99%

Limitations of information extraction methods and techniques for heterogeneous unstructured big data

Adnan

Akbar

2019

International Journal of Engineering Business Management

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During the recent era of big data, a huge volume of unstructured data are being produced in various forms of audio, video, images, text, and animation. Effective use of these unstructured big data is a laborious and tedious task. Information extraction (IE) systems help to extract useful information from this large variety of unstructured data. Several techniques and methods have been presented for IE from unstructured data. However, numerous studies conducted on IE from a variety of unstructured data are limited to single data types such as text, image, audio, or video. This article reviews the existing IE techniques along with its subtasks, limitations, and challenges for the variety of unstructured data highlighting the impact of unstructured big data on IE techniques. To the best of our knowledge, there is no comprehensive study conducted to investigate the limitations of existing IE techniques for the variety of unstructured big data. The objective of the structured review presented in this article is twofold. First, it presents the overview of IE techniques from a variety of unstructured data such as text, image, audio, and video at one platform. Second, it investigates the limitations of these existing IE techniques due to the heterogeneity, dimensionality, and volume of unstructured big data. The review finds that advanced techniques for IE, particularly for multifaceted unstructured big data sets, are the utmost requirement of the organizations to manage big data and derive strategic information. Further, potential solutions are also presented to improve the unstructured big data IE systems for future research. These solutions will help to increase the efficiency and effectiveness of the data analytics process in terms of context-aware analytics systems, data-driven decision-making, and knowledge management.

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“…Multiple distinct approaches have been attempted for automatically detecting drug abuse/misuse from social media chatter. For example, Jenhani et al 52 developed hybrid linguistic rules and a machine learning-based approach to detect drug-abuse-related tweets automatically. In our past work, 16 we aimed to investigate the opportunity of using social media as a resource for the automatic monitoring of prescription drug abuse by developing an automatic classification system that can classify possible abuse versus no-abuse posts.…”

Section: Related Workmentioning

confidence: 99%

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

Al-Garadi

Dan

Ruan

et al. 2020

Preprint

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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. 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. 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.

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A Hybrid Approach for Drug Abuse Events Extraction from Twitter

Cited by 18 publications

References 9 publications

Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records

Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records

Limitations of information extraction methods and techniques for heterogeneous unstructured big data

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

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