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Extracting Drug-Drug Interactions with Word and Character-Level Recurrent Neural Networks

Ramakanth Kavuluru, Anthony Rios, Tung Tran
2017 2017 IEEE International Conference on Healthcare Informatics (ICHI)  
However, to our knowledge, we are the first to investigate the potential of character-level RNNs (Char-RNNs) for DDI extraction (and relation extraction in general).  ...  In this paper, we explore recurrent neural network (RNN) architectures to detect and classify DDIs from unstructured text using the DDIExtraction dataset from the SemEval 2013 (task 9) shared task.  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.  ... 
doi:10.1109/ichi.2017.15 pmid:29034375 pmcid:PMC5639883 dblp:conf/ichi/KavuluruRT17 fatcat:xhybkccoqrgl3es5looqpngnde

An Attentive Sequence Model for Adverse Drug Event Extraction from Biomedical Text [article]

Suriyadeepan Ramamoorthy, Selvakumar Murugan
2018 arXiv   pre-print
, and to extract adverse reactions caused by a given drug.  ...  This enables us to visualize and understand how the network makes use of the local and wider context for classification.  ...  We concatenate the fixed and variable embeddings for word-level representation of sequence tokens. 2) Character-level Embedding: Convolutional Neural Networks (CNNs) have been shown to perform well on  ... 
arXiv:1801.00625v1 fatcat:sii57ub7g5bincnwc3gwq4gmd4

Combination of Deep Recurrent Neural Networks and Conditional Random Fields for Extracting Adverse Drug Reactions from User Reviews

Elena Tutubalina, Sergey Nikolenko
2017 Journal of Healthcare Engineering  
In this work, we propose a novel model for this problem, uniting recurrent neural architectures and conditional random fields.  ...  We evaluate our model with a comprehensive experimental study, showing improvements over state-of-the-art methods of ADR extraction.  ...  a character-level model.  ... 
doi:10.1155/2017/9451342 pmid:29177027 pmcid:PMC5605929 fatcat:jmjn4o3mtneilbuc37dt2gf5uq

GNTeam at 2018 n2c2: Feature-augmented BiLSTM-CRF for drug-related entity recognition in hospital discharge summaries [article]

Maksim Belousov, Nikola Milosevic, Ghada Alfattni, Haifa Alrdahi, Goran Nenadic
2019 arXiv   pre-print
The recurrent neural networks that use the pre-trained domain-specific word embeddings and a CRF layer for label optimization perform drug, adverse event and related entities extraction with micro-averaged  ...  We developed two deep learning architecture based on recurrent neural networks and pre-trained language models.  ...  The word representations were passed into the bidirectional recurrent neural network with long short-term memory units to learn important word-level features and transform them into the sequence label  ... 
arXiv:1909.10390v1 fatcat:nd7th73xlfap7latjetswrc5mm

LSTM-CRF for Drug-Named Entity Recognition

2017 Entropy  
Therefore, we offer an automatic exploring words and characters level features approach: a recurrent neural network using bidirectional long short-term memory (LSTM) with Conditional Random Fields decoding  ...  Drug-Named Entity Recognition (DNER) for biomedical literature is a fundamental facilitator of Information Extraction.  ...  Character level vector concatenated with word embedding as word representation.  ... 
doi:10.3390/e19060283 fatcat:23oswyhidzed7m3peajo2uuzdi

Machine learning-based identification and rule-based normalization of adverse drug reactions in drug labels

Mert Tiftikci, Arzucan Özgür, Yongqun He, Junguk Hur
2019 BMC Bioinformatics  
The machine learning approach is based on a recently proposed deep learning architecture, which integrates bi-directional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network (CNN), and Conditional  ...  Our study demonstrates that a system composed of a deep learning architecture for entity recognition and a rule-based model for entity normalization is a promising approach for ADR extraction from drug  ...  Disclaimer Part of the content described in this paper was presented at the TAC 2017 Workshop and published online as a non-peer reviewed conference proceedings paper.  ... 
doi:10.1186/s12859-019-3195-5 pmid:31865904 pmcid:PMC6927101 fatcat:pd253fjy3rbkzdyyulb24uyhsm

A hybrid Neural Network Model for Joint Prediction of Presence and Period Assertions of Medical Events in Clinical Notes

Li Rumeng, Jagannatha Abhyuday N, Yu Hong
2018 AMIA Annual Symposium Proceedings  
We formulate this hybrid neural network model (HNN), composed of recurrent neural network and deep residual network, to jointly predict the presence and period assertion values associated with medical  ...  In this paper, we propose a novel neural network architecture for clinical text mining.  ...  Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsor.  ... 
pmid:29854183 pmcid:PMC5977733 fatcat:zaelo5kn4zh7zbtcuuxj5wva7y

A neural joint model for entity and relation extraction from biomedical text

Fei Li, Meishan Zhang, Guohong Fu, Donghong Ji
2017 BMC Bioinformatics  
We demonstrate that the model based on neural networks is effective for biomedical entity and relation extraction.  ...  ., the task of extracting adverse drug events between drug and disease entities, and the task of extracting resident relations between bacteria and location entities.  ...  Acknowledgements The authors thank the anonymous referees for their careful reading of this manuscript and their extensive comments.  ... 
doi:10.1186/s12859-017-1609-9 pmid:28359255 pmcid:PMC5374588 fatcat:3tsz7w2kf5dcxgbrqhktiromgu

Attention-Gated Graph Convolutions for Extracting Drug Interaction Information from Drug Labels [article]

Tung Tran, Ramakanth Kavuluru, Halil Kilicoglu
2019 arXiv   pre-print
In this study, we tackle the problem of jointly extracting drugs and their interactions, including interaction outcome, from drug labels.  ...  As drug-drug interactions (DDIs) may lead to preventable adverse events, being able to extract DDIs from drug labels into a machine-processable form is an important step toward effective dissemination  ...  RK and TT were supported by the U.S. National Library of Medicine through grant R21LM012274.  ... 
arXiv:1910.12419v2 fatcat:nmb5j35aczcldg2h4ywoijiojy

A Multi-Task Learning Framework for Extracting Drugs and Their Interactions from Drug Labels [article]

Tung Tran, Ramakanth Kavuluru, Halil Kilicoglu
2019 arXiv   pre-print
As drug-drug interactions (DDIs) may cause adverse reactions, being able to extracting DDIs from drug labels into machine-readable form is an important effort in effectively deploying drug safety information  ...  Herein, we describe our approach to tackling tasks one and two of the DDI track, which corresponds to named entity recognition (NER) and sentence-level relation extraction respectively.  ...  RK and TT are also supported by the U.S. National Library of Medicine through grant R21LM012274.  ... 
arXiv:1905.07464v1 fatcat:w2oyrksnifctnjd5we6yk5o3qe

Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition

Iñigo Jauregi Unanue, Ehsan Zare Borzeshi, Massimo Piccardi
2017 Journal of Biomedical Informatics  
Conversely, more modern machine learning approaches such as recurrent neural networks (RNNs) have proved capable of automatically learning effective features from either random assignments or automated  ...  Previous state-of-the-art systems on Drug Name Recognition (DNR) and Clinical Concept Extraction (CCE) have focused on a combination of text "feature engineering" and conventional machine learning algorithms  ...  The bidirectional LSTM-CRF with word-level and character-level word embeddings.  ... 
doi:10.1016/j.jbi.2017.11.007 pmid:29146561 fatcat:hmqsrngy2fhgvd33h5iq7oosxa

Unified Neural Architecture for Drug, Disease and Clinical Entity Recognition [article]

Sunil Kumar Sahu, Ashish Anand
2017 arXiv   pre-print
Further our analysis of CRF layer and word-embedding obtained using character based embedding show their importance.  ...  ., in another words, same set of features are used in the three NER tasks, namely, disease name recognition (Disease NER), drug name recognition (Drug NER) and clinical entity recognition (Clinical NER  ...  Drug NER Identifying drug name or pharmacological substance are important first step for drug drug interaction extraction and for other drug related knowledge extraction tasks.  ... 
arXiv:1708.03447v1 fatcat:v5usafdx4bagflbzzwvgdyfsnm

Detecting Drug-Drug Interaction (DDI) over the Social Media using Convolution Neural Network Deep Learning

Kelechi Iwuorie, Computer Science Department, Lakehead University, Ontario, Canada, Sabah Mohammed*, Computer Science Department, Lakehead University, Ontario, Canada
2020 Asia-Pacific Journal of Neural Networks and Its Applications  
This paper presents a project extracting DDI from biomedical text using a Convolutional Neural Network (CNN) classifier.  ...  Drug-Drug Interaction (DDI) detection is a challenging problem for drug manufacturers, drug regulatory authorities, and medical professionals alike.  ...  The resurgence of deep neural networks has renewed interest in the application of such methods for DDI detection. [8] trained a Recurrent Neural Network (RNN) model on the DDI corpus, the study evaluated  ... 
doi:10.21742/ajnnia.2020.4.1.01 fatcat:rnchqnejwjfgdimda7ofzekqji

PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts

Jordi Amengol-Estapé, Felipe Soares, Montserrat Marimon, Martin Krallinger
2019 Genomics & Informatics  
neural networks.  ...  Automatically detecting mentions of pharmaceutical drugs and chemical substances is key for the subsequent extraction of relations of chemicals with other biomedical entities such as genes, proteins, diseases  ...  Recurrent neural networks (RNNs) are ANNs such that the computational graphs have cycles, which are used for dealing with sequences [13] .  ... 
doi:10.5808/gi.2019.17.2.e15 pmid:31307130 pmcid:PMC6808625 fatcat:aeoje3snjbgzjjkbkjhvr7srgq

Transforming unstructured voice and text data into insight for paramedic emergency service using recurrent and convolutional neural networks [article]

Kyongsik Yun, Thomas Lu, Alexander Huyen
2020 arXiv   pre-print
Then we used convolutional neural networks on top of custom-trained word vectors for sentence-level classification tasks.  ...  To train and test speech recognition models, we built a bidirectional deep recurrent neural network (long short-term memory (LSTM)).  ...  ACKNOWLEDGMENT The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.  ... 
arXiv:2006.04946v1 fatcat:5tzw6yeb3zcblbfbfqcc3merzi
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