Cadec: A corpus of adverse drug event annotations

By Sarvnaz Karimi, Alejandro Metke-Jimenez, Madonna Kemp, Chen Wang. Journal of Biomedical Informatics, 2015

298 citations recorded by the source corpus.

CSIRO Adverse Drug Event Corpus (Cadec) is a new rich annotated corpus of medical forum posts on patient-reported Adverse Drug Events (ADEs). The corpus is sourced from posts on social media, and contains text that is largely written in colloquial language and often deviates from formal English grammar and punctuation rules. Annotations contain mentions of concepts such as drugs, adverse effects, symptoms, and diseases linked to their corresponding concepts in controlled vocabularies, i.e., SNOMED Clinical Terms and MedDRA. The quality of the annotations is ensured by annotation guidelines, multi-stage annotations, measuring inter-annotator agreement, and final review of the annotations by a clinical terminologist. This corpus is useful for studies in the area of information extraction, or more generally text mining, from social media to detect possible adverse drug reactions from direct patient reports. The corpus is publicly available at https://data.csiro.au.(1).

Subfields: Computational Theory and Mathematics, Molecular Biology, Toxicology

View Paper: https://www.semanticscholar.org/paper/f13d7e3400c45049eb96d269d3de9bfd522a4b64

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Rania Kora
2. Xing Xie
3. Donghong Ji
4. Xipeng Qiu
5. Ammar Mohammed
6. Fei Li
7. Zheng Zhang
8. Meishan Zhang

In Molecular Biology

1. Rania Kora
2. Xing Xie
3. Xiang Dai
4. Heike Adel
5. Donghong Ji
6. Xipeng Qiu
7. Ammar Mohammed
8. Fei Li

In Toxicology

1. Rania Kora
2. Donghong Ji
3. Ammar Mohammed
4. Fei Li
5. Meishan Zhang

Citations in the corpus

• An Analysis of Simple Data Augmentation for Named Entity Recognition (2020, background): “This is especially true in some specific domains, such as the biomedical and materials science domain, where annotating data requires expert knowledge and is usually time-consuming (Karimi et al., 2015; Friedrich et al., 2020).”
• FedNER: Medical Named Entity Recognition with Federated Learning (2020, methodology): “We experiment on three publicly available medical NER datasets, e.g., C ADEC (Karimi et al., 2015), ADE Corpus (Gurulingappa et al., 2012) and SMM4H (Weissenbacher et al., 2019).”
• FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning (2020, methodology): “We experiment on three publicly available medical NER datasets, e.g., CADEC (Karimi et al., 2015), ADE Corpus (Gurulingappa et al., 2012) and SMM4H (Weissenbacher et al., 2019).”
• A Unified Generative Framework for Various NER Subtasks (2021, methodology): “Therefore, as shown in Table 4, the “Word” representation with smaller average entity length in CoNLL2003, OntoNotes, CADEC, ShARe13 achieves better performance in these datasets.”
• A comprehensive review on ensemble deep learning: Opportunities and challenges (2023, methodology): “In Akhtyamova et al. (2017) suggested a voting method based on CNNs for predicting drug safety using English reviews from health forums (Karimi et al., 2015).”
• Communication-efficient federated learning via knowledge distillation (2021, unknown): “We conduct experiment on three public benchmark medical NER datasets, e.g., CADEC 32 , ADE 33 and SMM4H 34 .”
• Unified Named Entity Recognition as Word-Word Relation Classification (2021, methodology): “At last, when the NNW relation is removed, the F1s on all datasets drop, especially on the CADEC (4.2%).”