Automagically encoding Adverse Drug Reactions in MedDRA
Pharmacovigilance is the field of science devoted to the collection, analysis and prevention of Adverse Drug Reactions (ADRs). Efficient strategies for the extraction of information about ADRs from free text resources are essential to support the work of experts, employed in the crucial task of detecting and classifying unexpected pathologies possibly related to drug assumptions. Narrative ADR descriptions may be collected in several way, e.g. by monitoring social networks or through the so called spontaneous reporting, the main method pharmacovigilance adopts in order to identify ADRs. The encoding of free-text ADR descriptions according to MedDRA standard terminology is central for report analysis. It is a complex work, which has to be manually implemented by the pharmacovigilance experts. The manual encoding is expensive (in terms of time). Moreover, a problem about the accuracy of the encoding may occur, since the number of reports is growing up day by day. In this paper, we propose MagiCoder, an efficient Natural Language Processing algorithm able to automatically derive MedDRA terminologies from free-text ADR descriptions. MagiCoder is part of VigiWork, a web application for online ADR reporting and analysis. From a practical view-point, MagiCoder radically reduces the revision time of ADR reports: the pharmacologist has simply to revise and validate the automatic solution versus the hard task of choosing solutions in the 70k terms of MedDRA. This improvement of the expert work efficiency has a meaningful impact on the quality of data analysis. Moreover, our procedure is general purpose. We developed MagiCoder for the Italian pharmacovigilance language, but preliminarily analyses show that it is robust to language and dictionary changes.
Code (0)
등록된 구현이 없습니다.
Tasks
PharmacovigilanceSimilar Papers 제목 키워드 기반
From narrative descriptions to MedDRA: automagically encoding adverse drug reactions
The collection of narrative spontaneous reports is an irreplaceable source for the prompt detection of suspected adverse drug reactions (ADRs): qualified domain experts manually revise a huge amount of narrative descript…
PharmacovigilanceConcept Extraction to Identify Adverse Drug Reactions in Medical Forums: A Comparison of Algorithms
Social media is becoming an increasingly important source of information to complement traditional pharmacovigilance methods. In order to identify signals of potential adverse drug reactions, it is necessary to first ide…
BIG-bench Machine LearningPharmacovigilanceWant to Identify, Extract and Normalize Adverse Drug Reactions in Tweets? Use RoBERTa
This paper presents our approach for task 2 and task 3 of Social Media Mining for Health (SMM4H) 2020 shared tasks. In task 2, we have to differentiate adverse drug reaction (ADR) tweets from nonADR tweets and is treated…
Binary ClassificationGeneral ClassificationLanguage ModelingLanguage Modelling+2UB Health Miners@SMM4H’22: Exploring Pre-processing Techniques To Classify Tweets Using Transformer Based Pipelines.
Here we discuss our implementation of two tasks in the Social Media Mining for Health Applications (SMM4H) 2022 shared tasks – classification, detection, and normalization of Adverse Events (AE) mentioned in English twee…
Binary ClassificationClassificationMulti-class Classificationnamed-entity-recognition+3Automated Generation of Custom MedDRA Queries Using SafeTerm Medical Map
In pre-market drug safety review, grouping related adverse event terms into standardised MedDRA queries or the FDA Office of New Drugs Custom Medical Queries (OCMQs) is critical for signal detection. We present a novel q…