Filters








2,210 Hits in 8.1 sec

Joint Entity Extraction and Assertion Detection for Clinical Text [article]

Parminder Bhatia, Busra Celikkaya, Mohammed Khalilia
2019 arXiv   pre-print
., named entity recognition (NER) and rule-based negation detection. We consider this as a multi-task problem and present a novel end-to-end neural model to jointly extract entities and negations.  ...  negation detection on the 2010 i2b2/VA challenge dataset and a proprietary de-identified clinical dataset.  ...  To the best of our knowledge, this is the first work to jointly model named entity and negation in an end-to-end system.  ... 
arXiv:1812.05270v3 fatcat:n4af6jwd7bbifnytnbpgaskf3m

Joint Entity Extraction and Assertion Detection for Clinical Text

Parminder Bhatia, Busra Celikkaya, Mohammed Khalilia
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
., named entity recognition (NER) and rule-based negation detection. We consider this as a multi-task problem and present a novel end-to-end neural model to jointly extract entities and negations.  ...  negation detection on the 2010 i2b2/VA challenge dataset and a proprietary de-identified clinical dataset.  ...  To the best of our knowledge, this is the first work to jointly model named entity and negation in an end-to-end system.  ... 
doi:10.18653/v1/p19-1091 dblp:conf/acl/BhatiaCK19 fatcat:a4llljv5mzdlhmsngaa2jzpife

Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning [article]

Mengqi Jin, Mohammad Taha Bahadori, Aaron Colak, Parminder Bhatia, Busra Celikkaya, Ram Bhakta, Selvan Senthivel, Mohammed Khalilia, Daniel Navarro, Borui Zhang, Tiberiu Doman, Arun Ravi, Matthieu Liger (+1 others)
2018 arXiv   pre-print
We leverage an internal medical natural language processing service to perform named entity extraction and negation detection on clinical notes and compose selected entities into a new text corpus to train  ...  We then propose a multimodal neural network to jointly train time series signals and unstructured clinical text representations to predict the in-hospital mortality risk for ICU patients.  ...  Our work is distinguishable from other studies in three aspects: 1) We leverage a medical natural language processing service for named entity recognition and negation detection to process text beyond  ... 
arXiv:1811.12276v2 fatcat:nkvmgs4hvzbcjgpl4ecfavd3gq

Integrating Speculation Detection and Deep Learning to Extract Lung Cancer Diagnosis from Clinical Notes

Oswaldo Solarte Pabón, Maria Torrente, Mariano Provencio, Alejandro Rodríguez-Gonzalez, Ernestina Menasalvas
2021 Applied Sciences  
Our findings suggest that speculation detection is together with negation detection a key component to properly extract cancer diagnosis from clinical notes.  ...  The approach integrates three steps: (i) lung cancer named entity recognition, (ii) negation and speculation detection, and (iii) relating the cancer diagnosis to a valid date.  ...  Cue Detection in Clinical Texts This step detects speculation and negation cues in a clinical text. Cue detection receives two inputs: a text sentence and the cues lexicon (Figure 4 ).  ... 
doi:10.3390/app11020865 fatcat:odpnldls7jhetgh23zvss5lm6y

KLOSURE: Closing in on open–ended patient questionnaires with text mining

Irena Spasić, David Owen, Andrew Smith, Kate Button
2019 Journal of Biomedical Semantics  
We implemented KLOSURE as a system for mining free-text responses to the KLOG questionnaire.  ...  Feature extraction is performed by a set of four modules whose main functionalities are linguistic pre-processing, sentiment analysis, named entity recognition and lexicon lookup respectively.  ...  In summary, speed, accuracy, ease of use and accessibility make voice-to-text software a great user-friendly tool for patients to respond to open-ended questions.  ... 
doi:10.1186/s13326-019-0215-3 pmid:31711536 pmcid:PMC6849171 fatcat:2ok6stwjyfchrpnuif64zdu6wy

Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text

Jun Xu, Zhiheng Li, Qiang Wei, Yonghui Wu, Yang Xiang, Hee-Jin Lee, Yaoyun Zhang, Stephen Wu, Hua Xu
2019 BMC Medical Informatics and Decision Making  
To detect attributes of medical concepts in clinical text, a traditional method often consists of two steps: named entity recognition of attributes and then relation classification between medical concepts  ...  Here we present a novel solution, in which attribute detection of given concepts is converted into a sequence labeling problem, thus attribute entity recognition and relation classification are done simultaneously  ...  Acknowledgments The authors would like to thank the organizers of the i2b2 2009, i2b2 2010, CLEF eHealth 2014, SemEval 2015 Task 14 for providing the datasets.  ... 
doi:10.1186/s12911-019-0937-2 pmid:31801529 pmcid:PMC6894107 fatcat:qg6coyh6wfgjtbvbou4fhywcp4

Learning Structures of Negations from Flat Annotations

Vinodkumar Prabhakaran, Branimir Boguraev
2015 Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics  
We achieve high accuracy for negation detection overall, 87%.  ...  We succeed in training a model for detecting the negated predicates corresponding to the annotated negations, by re-mapping the corpus to anchor its 'flat' annotation spans into the predicate argument  ...  We thank several anonymous reviewers for their constructive feedback.  ... 
doi:10.18653/v1/s15-1008 dblp:conf/starsem/PrabhakaranB15 fatcat:jxg3w334dfea7jvl3anwe43gp4

Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks

Savelie Cornegruta, Robert Bakewell, Samuel Withey, Giovanni Montana
2016 Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis  
The model has been used to address two NLP tasks: medical named-entity recognition (NER) and negation detection.  ...  Motivated by the need to automate medical information extraction from free-text radiological reports, we present a bi-directional long short-term memory (BiLSTM) neural network architecture for modelling  ...  For negation detection, the system first obtained NegEx predictions for the entities extracted in the NER task.  ... 
doi:10.18653/v1/w16-6103 dblp:conf/acl-louhi/CornegrutaBWM16 fatcat:ub54zeiw6jbc3ec36shvucr65y

FLE at CLEF eHealth 2020: Text Mining and Semantic Knowledge for Automated Clinical Encoding

Nuria García-Santa, Kendrick Cetina
2020 Conference and Labs of the Evaluation Forum  
In the present paper, we show our approach which is based on Named Entity Recognition (NER) to detect the diagnoses and procedures, and semantic linking against a Knowledge Graph to extract the ICD-10  ...  The objective of the tasks of the 2020 CLEF eHealth for Multilingual Information Extraction is to develop solutions to automatically annotate Spanish clinical texts with codes from the International Classification  ...  and positions of predicted entities. Our approach achieves F1-Scores of 0.67, 0.51 and 0.61 for tasks 1, 2 and 3 respectively.  ... 
dblp:conf/clef/Garcia-SantaC20 fatcat:7byceqsb4jdgbdi67knw3gvz4i

End-to-End Extraction of Family History Information from Clinical Notes Using Deep Learning and Heuristics (Preprint)

João Figueira Silva, João Rafael Almeida, Sérgio Matos
2020 JMIR Medical Informatics  
, using deep learning (DL) approaches for the extraction of entities from clinical notes, and combining both approaches in a hybrid end-to-end system capable of successfully extracting family member and  ...  We evaluated the impact of several factors on the performance of DL models, and we present an end-to-end system for extracting family history information from clinical notes, which can help in the structuring  ...  Precision, recall, and F1-scores are provided separately for family member and observation extraction along with overall results. [PDF File (Adobe PDF File), 72 KB-Multimedia Appendix 1]  ... 
doi:10.2196/22898 pmid:33372893 fatcat:jp6ifgucang6ricqfjavijiixy

Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks [article]

Savelie Cornegruta, Robert Bakewell, Samuel Withey, Giovanni Montana
2016 arXiv   pre-print
The model has been used to address two NLP tasks: medical named-entity recognition (NER) and negation detection.  ...  Motivated by the need to automate medical information extraction from free-text radiological reports, we present a bi-directional long short-term memory (BiLSTM) neural network architecture for modelling  ...  For negation detection, the system first obtained NegEx predictions for the entities extracted in the NER task.  ... 
arXiv:1609.08409v1 fatcat:h4t53ualwrflvcrdbcufweajxu

DEEPEN: A negation detection system for clinical text incorporating dependency relation into NegEx

Saeed Mehrabi, Anand Krishnan, Sunghwan Sohn, Alexandra M. Roch, Heidi Schmidt, Joe Kesterson, Chris Beesley, Paul Dexter, C. Max Schmidt, Hongfang Liu, Mathew Palakal
2015 Journal of Biomedical Informatics  
Natural language processing (NLP) techniques have been developed to extract clinical information from free text.  ...  A negation detection algorithm, NegEx, applies a simplistic approach that has been shown to be powerful in clinical NLP.  ...  Acknowledgments This work was supported in part by the agency for healthcare research and quality R01 HS19818-01, grant from the office of the vice president for research at IUPUI and a joint funding from  ... 
doi:10.1016/j.jbi.2015.02.010 pmid:25791500 pmcid:PMC5863758 fatcat:connw37ws5fdjp3kxmx3kvwija

Bleeding Entity Recognition in Electronic Health Records: A Comprehensive Analysis of End-to-End Systems

Avijit Mitra, Bhanu Pratap Singh Rawat, David McManus, Alok Kapoor, Hong Yu
2021 AMIA Annual Symposium Proceedings  
Our error analyses suggest that the models' incorrect predictions can be attributed to variability in entity spans, memorization, and missing negation signals.  ...  A bleeding event is a common adverse drug reaction amongst patients on anticoagulation and factors critically into a clinician's decision to prescribe or continue anticoagulation for atrial fibrillation  ...  Acknowledgement We would like to thank our annotators -Raelene Goodwin, Edgard Granillo, Nadiya Frid, Heather Keating and Brian Corner for annotating the discharge summaries.  ... 
pmid:33936461 pmcid:PMC8075442 fatcat:jwhasrhkwrfwzdqzrd6ceulnou

Automatic negation detection in narrative pathology reports

Ying Ou, Jon Patrick
2015 Artificial Intelligence in Medicine  
The negation and uncertainty detection modules were built to handle this problem.  ...  The goal of this thesis is to extract pertinent information from free-text pathology reports and automatically populate structured reports for three cancer diseases, namely melanoma, colorectal cancer,  ...  Generally, negation detection includes the detection of negation cues (specific terms to indicate negation) and their scope (the text negated by the terms).  ... 
doi:10.1016/j.artmed.2015.03.001 pmid:25990897 fatcat:yrijkncnsvht7lonmaqt7uyya4

NEAR: Named Entity and Attribute Recognition of clinical concepts

Namrata Nath, Sang-Heon Lee, Ivan Lee
2022 Journal of Biomedical Informatics  
Named Entity Recognition (NER) or the extraction of concepts from clinical text is the task of identifying entities in text and slotting them into categories such as problems, treatments, tests, clinical  ...  This research hopes to contribute to the area of detecting entities and their corresponding attributes by modelling the NER task as a supervised, multi-label tagging problem with each of the attributes  ...  ACKNOWLEDGEMENTS We would like to thank A/Prof. Mark D McDonnell for making the resources needed for our experimentation available. APPENDIX A.  ... 
doi:10.1016/j.jbi.2022.104092 pmid:35533990 fatcat:nlvj5zxyqne5nfcvupjr3bzrya
« Previous Showing results 1 — 15 out of 2,210 results