Identifying Adverse Drug Events Mentions in Tweets Using Attentive, Collocated, and Aggregated Medical Representation

Zhao, Xinyan; Yu, Deahan; Vydiswaran, V. G. Vinod

doi:10.18653/v1/w19-3209

Cited by 3 publications

(7 citation statements)

References 18 publications

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“…By running our models on the validation set shown in Table 2, we confirmed that the performance of CLAPA was almost the same as that of previously published models, with an F1 score of 0.5998 [26]. The performance of BERT was also similar to that of BERT-based models reported in an overview of the SMM4H 2019 shared task [13].…”

Section: Principal Results Of the Nlp Systemsupporting

confidence: 80%

“…In our previous work, we proposed a collocated LSTM model with attentive pooling and aggregated representation (CLAPA) that utilized neighborhood information to build a better representation of medical concepts [ 26 ]. The model focused on enhancing medical concepts by incorporating neighborhood information through a collocation graph.…”

Section: Methodsmentioning

confidence: 99%

“…CLAPA [ 26 ], illustrated in Figure 1 A, uses collocation information to improve the representation of medical concepts. CLAPA requires three main components: (1) medical concepts, (2) a collocation graph, and (3) a model architecture.…”

Section: Methodsmentioning

confidence: 99%

“…Second, for the collocation graph, each unique word in the training set was assigned as a node, and edges were added between node pairs if the corresponding pair of words were adjacent to each other. After the graph was constructed, the graph was reduced by retaining only the closest 15 neighbor nodes per medical concept, following an empirical analysis of neighborhood size [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

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An Assessment of Mentions of Adverse Drug Events on Social Media With Natural Language Processing: Model Development and Analysis

Yu¹,

Vydiswaran²

2022

JMIR Med Inform

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Background Adverse reactions to drugs attract significant concern in both clinical practice and public health monitoring. Multiple measures have been put into place to increase postmarketing surveillance of the adverse effects of drugs and to improve drug safety. These measures include implementing spontaneous reporting systems and developing automated natural language processing systems based on data from electronic health records and social media to collect evidence of adverse drug events that can be further investigated as possible adverse reactions. Objective While using social media for collecting evidence of adverse drug events has potential, it is not clear whether social media are a reliable source for this information. Our work aims to (1) develop natural language processing approaches to identify adverse drug events on social media and (2) assess the reliability of social media data to identify adverse drug events. Methods We propose a collocated long short-term memory network model with attentive pooling and aggregated, contextual representation generated by a pretrained model. We applied this model on large-scale Twitter data to identify adverse drug event–related tweets. We conducted a qualitative content analysis of these tweets to validate the reliability of social media data as a means to collect such information. Results The model outperformed a variant without contextual representation during both the validation and evaluation phases. Through the content analysis of adverse drug event tweets, we observed that adverse drug event–related discussions had 7 themes. Mental health–related, sleep-related, and pain-related adverse drug event discussions were most frequent. We also contrast known adverse drug reactions to those mentioned in tweets. Conclusions We observed a distinct improvement in the model when it used contextual information. However, our results reveal weak generalizability of the current systems to unseen data. Additional research is needed to fully utilize social media data and improve the robustness and reliability of natural language processing systems. The content analysis, on the other hand, showed that Twitter covered a sufficiently wide range of adverse drug events, as well as known adverse reactions, for the drugs mentioned in tweets. Our work demonstrates that social media can be a reliable data source for collecting adverse drug event mentions.

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Section: Principal Results Of the Nlp Systemsupporting

confidence: 80%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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An Assessment of Mentions of Adverse Drug Events on Social Media With Natural Language Processing: Model Development and Analysis

Yu¹,

Vydiswaran²

2022

JMIR Med Inform

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“…Text mining and partially supervised learning methods [3] are integrated to classify ADR (positive instances) and non-ADR messages (negative instances), and researchers employ various features such as word embedding [4], position feature [5] and medical knowledge [6] to promote the whole performance of their methods. Moreover, researchers utilize attention mechanisms [7], transfer learning [8], co-training learning [9], broad learning [10] and multitask learning [11] to learn these deep dominant features [12]. Medical resources and emotional score are merged into features that represent the semantic meaning of the text segments of different methods.…”

Section: Introductionmentioning

confidence: 99%

An Effective Emotional Expression and Knowledge-Enhanced Method for Detecting Adverse Drug Reactions

Lin

Zheng

2020

IEEE Access

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Discovering more time-effective and a wider range of adverse drug reactions (ADRs) from social texts related to feelings concerning taking medication has recently received significant interest in pharmacovigilance research. Recognizing the posts that include ADRs is an important step for detecting ADRs from social texts. The existing systems show the unsatisfactory performance due to the insufficient expression of emotions and the inadequacy of information expression in short social texts. Although these systems exploit emotional features to improve the performance of their methods, the representation of wordlevel emotional scores is insufficient for emotional expression. Moreover, most of the systems make less use of medical knowledge to enhance the detection of the potential relationship between drugs and adverse reactions in posts. Therefore, enough expression of emotion and medical knowledge in sparse medical social texts may be explored to improve system performance. This paper proposed an effective method integrating sufficient emotional expression and medical knowledge to detect ADRs from medical tweets. First, the proposed method utilized sentence-level emotional context and word-level emotional score to learn sufficient emotional information for distinguishing between ADR and non-ADR tweets. Furthermore, a co-occurrence dictionary of each drug and its relevant ADRs was constructed by means of a medical resource (MedDRA) and drug site (www.drug.com) to help the proposed model focus on posts containing drugs and ADRs. Finally, a convolutional neural network (CNN) model on the basis of bidirectional encoder representations from transformers (BERT) performed the classification task. The proposed model achieved better overall performance than the other existing methods on two Twitter datasets (F1-scores of 72.64% and 64.98% on PSB2016 and SMM4H, respectively). INDEX TERMS Adverse drug reaction, medical knowledge, emotional context, co-occurrence dictionary, social text.

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Exploiting Question-Answer framework with multi-GRU to detect Adverse Drug Reaction on social media

Luo,

Yang

2024

Preprint

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Adverse Drug Reactions (ADRs) stand out as a pressing challenge in public health and a critical aspect of drug discovery. The dilemma arises from the inherent impossibility of conducting a comprehensive evaluation of a drug before its market release, constrained by the limitations in scale and duration of clinical trials. Therefore, the post-marketing detection of ADRs in a timely and accurate manner becomes imperative. Adding to the complexity, a multitude of tweets harbor concealed information about adverse drug reactions, creating difficulties due to their concise, sporadic, and noisy content. To solve the problem, we regard ADR detection as a question-answer problem and introduces an innovative neural network framework with multiple GRU layers designed for extracting ADR-related information from tweets. The Von Mises–Fisher distribution (vMF distribution) is applied to derive keyword vectors through tweet sampling. An attention mecha-nism is employed to enhance the interaction between these keyword vectors and the word sequences within tweets. The credibility of word sequences is systematically evaluated based on the reliability of answer factors. To address concerns related to background information and training speed, we propose a quality assurance mechanism utilizing a GRU network due to its straightforward structure and efficient training capabilities. As a result of the training process, word sequences are mapped to a low-latitude vector space, generating corresponding answers. Experimental results obtained from two Twitter ADR datasets affirm that our Question-Answer Mechanism, leveraging multi-GRU architecture, significantly improves the accuracy of ADR detection in tweets. Our method achieved F1-scores of 81.3% and 73.3% on the two datasets, respectively, while consistently maintaining a higher recall.

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Identifying Adverse Drug Events Mentions in Tweets Using Attentive, Collocated, and Aggregated Medical Representation

Cited by 3 publications

References 18 publications

An Assessment of Mentions of Adverse Drug Events on Social Media With Natural Language Processing: Model Development and Analysis

An Assessment of Mentions of Adverse Drug Events on Social Media With Natural Language Processing: Model Development and Analysis

An Effective Emotional Expression and Knowledge-Enhanced Method for Detecting Adverse Drug Reactions

Exploiting Question-Answer framework with multi-GRU to detect Adverse Drug Reaction on social media

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