• Corpus ID: 245704719

Mining Adverse Drug Reactions from Unstructured Mediums at Scale

@inproceedings{Haq2022MiningAD,
  title={Mining Adverse Drug Reactions from Unstructured Mediums at Scale},
  author={Hasham Ul Haq and Veysel Kocaman and David Talby},
  year={2022}
}
Adverse drug reactions / events (ADR/ADE) have a major impact on patient health and health care costs. Detecting ADR’s as early as possible and sharing them with regulators, pharma companies, and healthcare providers can prevent morbidity and save many lives. While most ADR’s are not reported via formal channels, they are often documented in a variety of unstructured conversations such as social media posts by patients, customer support call transcripts, or CRM notes of meetings between… 

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References

SHOWING 1-10 OF 34 REFERENCES

Towards Internet-Age Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts in Health-Related Social Networks

TLDR
It is concluded that user comments pose a significant natural language processing challenge, but do contain useful extractable information which merits further exploration and is evaluated on a manually annotated set of user comments with promising performance.

Cadec: A corpus of adverse drug event annotations

Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models

TLDR
This work uses the CADEC corpus to train a recurrent neural network (RNN) transducer, integrated with knowledge graph embeddings of DBpedia, and shows the resulting model to be highly accurate.

Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach

TLDR
This study proposes a causality measure that can detect an adverse reaction that is caused by a drug rather than merely being a correlated signal, and obtains an ADR detection accuracy of 74% on a large-scale manually annotated dataset of tweets.

Deeper Clinical Document Understanding Using Relation Extraction

TLDR
A text mining framework comprising of Named Entity Recognition and Relation Extraction models, which expands on previous work in three main ways and shows two practical applications – for building a biomedical knowledge graph and for improving the accuracy of mapping entities to clinical codes.

2018 N2c2 Shared Task on Adverse Drug Events and Medication Extraction in Electronic Health Records

TLDR
This challenge shows that clinical concept extraction and relation classification systems have a high performance for many concept types, but significant improvement is still required for ADEs and Reasons.

BERT based Adverse Drug Effect Tweet Classification

This paper describes models developed for the Social Media Mining for Health (SMM4H) 2021 shared tasks. Our team participated in the first subtask that classifies tweets with Adverse Drug Effect

Entity-Level Classification of Adverse Drug Reaction: A Comparative Analysis of Neural Network Models

TLDR
The applicability of the neural network models developed for the aspect-level sentiment analysis to the problem of the classification of adverse drug reactions is studied and a best model based on the support vector machine method and a large set of features is compared.

Adverse Drug Reaction Classification With Deep Neural Networks

TLDR
Two new neural network models are proposed by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural Network with Attention (CNNA), which allows the visualisation of attention weights of words when making classification decisions and hence is more appropriate for the extraction of word subsequences describing ADRs.