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Bidirectional Recurrent Neural Networks for Medical Event Detection in Electronic Health Records [article]

Abhyuday Jagannatha, Hong Yu
2016 arXiv   pre-print
Sequence labeling for extraction of medical events and their attributes from unstructured text in Electronic Health Record (EHR) notes is a key step towards semantic understanding of EHRs.  ...  In this application, we explored various recurrent neural network frameworks and show that they significantly outperformed the CRF models.  ...  We also thank the anonymous reviewers for their comments and suggestions. This work was supported in part by the grant 5U01CA180975 from the National Institutes of Health (NIH).  ... 
arXiv:1606.07953v2 fatcat:hsng6xtcpzfujpb5fgrz4j6agy

High-Performing Systems for Automatically Detecting Hypoglycemic Events from Electronic Health Record Notes (Preprint)

Yonghao Jin, Fei Li, Hong Yu
2019 JMIR Medical Informatics  
Automatic detection of such events could improve patient care and is valuable in population studies. Electronic health records (EHRs) are valuable resources for the detection of such events.  ...  We found that neural network models outperformed the SVM model.  ...  Recurrent Neural Network Recurrent Neural Network (RNN) is a common type of neural networks used for sequential data.  ... 
doi:10.2196/14340 pmid:31702562 pmcid:PMC6913754 fatcat:spwbvlqu4jeevdvadphby5yyiu

Structured prediction models for RNN based sequence labeling in clinical text [article]

Abhyuday Jagannatha, Hong Yu
2016 arXiv   pre-print
In clinical domain one major application of sequence labeling involves extraction of medical entities such as medication, indication, and side-effects from Electronic Health Record narratives.  ...  Sequence labeling in this domain, presents its own set of challenges and objectives. In this work we experimented with various CRF based structured learning models with Recurrent Neural Networks.  ...  We also thank the anonymous reviewers for their comments and suggestions. This work was supported in part by the grant HL125089 from the National Institutes of Health (NIH).  ... 
arXiv:1608.00612v1 fatcat:xrqigaxlgjhyhovcdjfvmq34yy

Detecting Medications and Adverse Drug Events in Clinical Notes Using Recurrent Neural Networks

Xi Yang, Jiang Bian, Yonghui Wu
2018 International Workshop on Medication and Adverse Drug Event Detection  
Early detection of Adverse Drug Events (ADEs) from Electronic Health Records (EHRs) is an important, challenging task to support pharmacovigilance and drug safety surveillance.  ...  The authors present a Recurrent Neural Network (RNN)-based system to detect medication name and its attributes (dosage, frequency, route, duration), as well as mentions of ADEs, Indications, other signs  ...  Conclusion This study presents our clinical NER system developed in the MADE1.0 open challenge.  ... 
dblp:conf/medaded/YangBW18 fatcat:lgdpwx6dqfc5pgmr7oux72y2xi

Time Series Prediction using Deep Learning Methods in Healthcare [article]

Mohammad Amin Morid, Olivia R. Liu Sheng, Joseph Dunbar
2021 arXiv   pre-print
In this paper we systematically reviewed studies focused on advancing and using deep neural networks to leverage patients structured time series data for healthcare prediction tasks.  ...  Second, these methods depend on feature engineering to capture the sequential nature of patient data, which may not adequately leverage the temporal patterns of the medical events and their dependencies  ...  ("electronic health record" OR "electronic health records" OR "EHR" OR "EHRs" OR "electronic medical record" OR "electronic medical records" OR "EMR" OR "EMRs" OR "medical claim" OR "medical claims" OR  ... 
arXiv:2108.13461v2 fatcat:3e7t5r5qivaszhigvuls6wknd4

Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach

Rumeng Li, Baotian Hu, Feifan Liu, Weisong Liu, Francesca Cunningham, David D McManus, Hong Yu
2019 JMIR Medical Informatics  
As bleeding events are often described in clinical notes, automatic detection of bleeding events from electronic health record (EHR) notes may improve drug-safety surveillance and pharmacovigilance.  ...  We developed an innovative hybrid convolutional neural network (CNN) and long short-term memory (LSTM) autoencoder (HCLA) model that integrates a CNN architecture with a bidirectional LSTM (BiLSTM) autoencoder  ...  This study was supported, in part, by the grant R01HL12508 and R01HL125089 from the National Institutes of Health.  ... 
doi:10.2196/10788 pmid:30735140 pmcid:PMC6384542 fatcat:zzpyzn3bhzfqjfeh6mwdimzyhu

Structured prediction models for RNN based sequence labeling in clinical text

Abhyuday Jagannatha, hong yu
2016 Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing  
In this work we experiment with Conditional Random Field based structured learning models with Recurrent Neural Networks.  ...  In the clinical domain one major application of sequence labeling involves extraction of relevant entities such as medication, indication, and side-effects from Electronic Health Record Narratives.  ...  We also thank the anonymous reviewers for their comments and suggestions. This work was supported in part by the grant HL125089 from the National Institutes of Health (NIH).  ... 
doi:10.18653/v1/d16-1082 dblp:conf/emnlp/JagannathaY16 fatcat:3ne5lqrcwfckjatpvavebbopna

Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review

Cao Xiao, Edward Choi, Jimeng Sun
2018 JAMIA Journal of the American Medical Informatics Association  
Objective: To conduct a systematic review of deep learning models for electronic health record (EHR) data, and illustrate various deep learning architectures for analyzing different data sources and their  ...  Discussion: Despite the early success in using deep learning for health analytics applications, there still exist a number of issues to be addressed.  ...  For instance, in 27 , 610 076 patient records from Vanderbilt's Electronic Medical Record were used to perform sequential prediction of medications.  ... 
doi:10.1093/jamia/ocy068 pmid:29893864 fatcat:ne7weiw7xvc2lp7hfgkzltdnri

Bidirectional LSTM-CRF for Adverse Drug Event Tagging in Electronic Health Records

Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Elke A. Rundensteiner, Xiangnan Kong
2018 International Workshop on Medication and Adverse Drug Event Detection  
Electronic health records (EHRs) of patients in hospitals contain valuable information regarding the ADEs and hence are an important source for detecting ADE signals.  ...  Adverse drug event (ADE) detection is a vital step towards effective pharmacovigilance and prevention of future incidents caused by potentially harmful ADEs.  ...  Sahoo and Thang La, Regulatory Science, OSE, FDA for introducing us to Pharmacovigilance in general and the ADE detection problem in particular.  ... 
dblp:conf/medaded/WunnavaQKRK18 fatcat:zybmatl7lba2nf7amfpwm4yxpq

Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning

Tsendsuren Munkhdalai, Feifan Liu, Hong Yu
2018 JMIR Public Health and Surveillance  
Medication and adverse drug event (ADE) information extracted from electronic health record (EHR) notes can be a rich resource for drug safety surveillance.  ...  For the neural network system, we exploited the state-of-the-art recurrent neural network (RNN) and attention models.  ...  Acknowledgments This work was supported by the grant R01HL125089 from the National Institutes of Health.  ... 
doi:10.2196/publichealth.9361 pmid:29695376 pmcid:PMC5943628 fatcat:ehfpn3dq35hvlbver4uzzr4h3e

Deep Bidirectional Recurrent Neural Networks as End-To-End Models for Smoking Status Extraction from Clinical Notes in Spanish [article]

Santiago Esteban, Manuel Rodriguez Tablado, Francisco Emiliano Peper, Sergio Adrian Terrasa, Karin Silvana Kopitowski
2018 bioRxiv   pre-print
Methods: We compared the performance of two strategies for labeling clinical notes of an electronic medical record in Spanish according to the patient's smoking status (current smoker, current non-smoker  ...  Deep recurrent neural networks (RNNs) have been proposed as 'end-to-end' models that learn both variables and parameters jointly, thus avoiding manual feature engineering and saving development time.  ...  Bidirectional Long Short Term Memory (LSTM) Recurrent Neural Networks (RNN) as End-to-End models for text classification Recurrent neural networks are a type of artificial neural network architecture that  ... 
doi:10.1101/320846 fatcat:7v4ekywprrc2fctuzx6djmr72y

Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record

Jinghe Zhang, Kamran Kowsari, James H. Harrison, Jennifer M. Lobo, Laura E. Barnes
2018 IEEE Access  
The wide implementation of electronic health record (EHR) systems facilitates the collection of large-scale health data from real clinical settings.  ...  Despite the significant increase in adoption of EHR systems, this data remains largely unexplored, but presents a rich data source for knowledge discovery from patient health histories in tasks such as  ...  Deep Representation of the Longitudinal Electronic Health Record  ... 
doi:10.1109/access.2018.2875677 fatcat:zumaqz3pbrflfmm72mmes2rxji

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.  ...  Text reviews, either on specialized web sites or in general-purpose social networks, may lead to a data source of unprecedented size, but identifying ADRs in free-form text is a challenging natural language  ...  NLP techniques have been applied in five main domain of texts: (i) biomedical literature, clinical trial records, and electronic medical/health records (e.g., medical correspondence and letters) [3, 5  ... 
doi:10.1155/2017/9451342 pmid:29177027 pmcid:PMC5605929 fatcat:jmjn4o3mtneilbuc37dt2gf5uq

Comparative analysis of context representation models in the relation extraction task from biomedical texts

Ilseyar Alimova, Elena Tutubalina
2019 International Young Scientists Conference on Information Technologies, Telecommunications and Control Systems  
We conduct a set of experiments on two benchmark corpora of patient electronic health records and scientific articles in English.  ...  We compare several context representation methods such as a bag of words representation, average word embeddings, sentence embedding, representations obtained by convolutional, recurrent neural networks  ...  Acknowledgments This research was supported by the Russian Foundation for Basic Research grant no. 190701115.  ... 
dblp:conf/ittcs/AlimovaT19 fatcat:sj6b6kpxmrdereyh7ybudwfapy

Named Entity Recognition using Neural Networks for Clinical Notes

Edson Flórez, Frédéric Precioso, Michel Riveill, Romaric Pighetti
2018 International Workshop on Medication and Adverse Drug Event Detection  
Currently, the best performance for Named Entity Recognition in medical notes is obtained by systems based on neural networks.  ...  These supervised systems require precise features in order to learn well fitted models from training data, for the purpose of recognizing medical entities like medication and Adverse Drug Events (ADE).  ...  For the purpose of identifying ADE mentions, we use medical notes provided in EHR (Electronic Health Records).  ... 
dblp:conf/medaded/FlorezPRP18 fatcat:t7rxpn2tzrblvfaalrcgloudke
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