Automated detection of adverse drug reactions in the biomedical literature using convolutional neural networks and biomedical word embeddings
DS Miranda - arXiv preprint arXiv:1804.09148, 2018 - arxiv.org
… models as well as Long Short Term Memory (LSTM) models. … of word embeddings specifically
developed for biomedical text … of word embeddings developed specifically for biomedical …
developed for biomedical text … of word embeddings developed specifically for biomedical …
Improving RNN with attention and embedding for adverse drug reactions
… In this paper, we evaluate the use of word embeddings on the … efficient word embeddings
form known clinical entities (drug … the biomedical concepts as a target word embedding matrix …
form known clinical entities (drug … the biomedical concepts as a target word embedding matrix …
Detecting adverse drug reactions on Twitter with convolutional neural networks and word embedding features
… as represented by word vectors created using unsupervised … (SVM) models that incorporated
word embedding, n-gram, and … allows it to evaluate every word embedding in a given tweet …
word embedding, n-gram, and … allows it to evaluate every word embedding in a given tweet …
Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features
… Considering the challenges of task-specific feature design, and given the success of the
deep learning techniques in generating the word embeddings, our future work will involve …
deep learning techniques in generating the word embeddings, our future work will involve …
[HTML][HTML] Multimodal model with text and drug embeddings for adverse drug reaction classification
A Sakhovskiy, E Tutubalina - Journal of Biomedical Informatics, 2022 - Elsevier
… stop words using stopwords lists obtained from NLTK. Domain-specific biomedical English
… FastText 7 model were used to obtain word embeddings for the CNN baseline. Each CNN …
… FastText 7 model were used to obtain word embeddings for the CNN baseline. Each CNN …
[HTML][HTML] Development of a pipeline for adverse drug reaction identification in clinical notes: word embedding models and string matching
KR Siegersma, M Evers, SH Bots… - JMIR medical …, 2022 - medinform.jmir.org
… drug reactions (ADRs) in the population is limited because of underreporting, which hampers
surveillance and assessment of drug … extraction of medication and Adverse Drug Reaction …
surveillance and assessment of drug … extraction of medication and Adverse Drug Reaction …
BioReddit: Word embeddings for user-generated biomedical NLP
M Basaldella, N Collier - … of the Tenth International Workshop on …, 2019 - aclanthology.org
… word embeddings with biomedical data, we are not aware of any publicly available word
embeddings trained … “contains patients expression of effectiveness and adverse drug events as- …
embeddings trained … “contains patients expression of effectiveness and adverse drug events as- …
An attentive neural sequence labeling model for adverse drug reactions mentions extraction
… For PubMed biomedical text dataset, we set the epoch to 10, the batch size to 16, and the …
For the PubMed dataset, we use the drug related word embeddings trained with PubMed …
For the PubMed dataset, we use the drug related word embeddings trained with PubMed …
DeepCADRME: A deep neural model for complex adverse drug reaction mentions extraction
… Drug Reaction (ADR) from biomedical texts, aiming to support pharmacovigilance and drug
safety … The first one combines the character embedding with CNN, the word embedding with …
safety … The first one combines the character embedding with CNN, the word embedding with …
Exploring joint AB-LSTM with embedded lemmas for adverse drug reaction discovery
S Santiso, A Perez, A Casillas - IEEE journal of biomedical and …, 2018 - ieeexplore.ieee.org
… Turning to practical details, we used pre-trained wordembeddings and for the distances the
embeddings were initialized with random values. There are antecedents on binning features …
embeddings were initialized with random values. There are antecedents on binning features …
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