End-to-End Joint Entity Extraction and Negation Detection for Clinical Text

2019 ◽  
pp. 139-148 ◽  
Author(s):  
Parminder Bhatia ◽  
E. Busra Celikkaya ◽  
Mohammed Khalilia
Keyword(s):  
Entity Extraction ◽  
Clinical Text ◽  
Negation Detection ◽  
End To End

Evaluation of lexicon- and syntax-based negation detection algorithms using clinical text data

2017 ◽  
Vol 13 (4) ◽  
Author(s):  
J. Manimaran ◽  
T. Velmurugan
Keyword(s):  
Language Processing ◽  
Patient Discharge ◽  
Computational Time ◽  
Practical Implementation ◽  
Automatic Identification ◽  
Kappa Statistics ◽  
Research Approach ◽  
Clinical Text ◽  
Medical Reports ◽  
Negation Detection

AbstractBackground:Clinical Text Analysis and Knowledge Extraction System (cTAKES) is an open-source natural language processing (NLP) system. In recent development modules of cTAKES, a negation detection (ND) algorithm is used to improve annotation capabilities and simplify automatic identification of negative context in large clinical documents. In this research, the two types of ND algorithms used are lexicon and syntax, which are analyzed using a database made openly available by the National Center for Biomedical Computing. The aim of this analysis is to find the pros and cons of these algorithms.Methods:Patient medical reports were collected from three institutions included the 2010 i2b2/VA Clinical NLP Challenge, which is the input data for this analysis. This database includes patient discharge summaries and progress notes. The patient data is fed into five ND algorithms: NegEx, ConText, pyConTextNLP, DEEPEN and Negation Resolution (NR). NegEx, ConText and pyConTextNLP are lexicon-based, whereas DEEPEN and NR are syntax-based. The results from these five ND algorithms are post-processed and compared with the annotated data. Finally, the performance of these ND algorithms is evaluated by computing standard measures including F-measure, kappa statistics and ROC, among others, as well as the execution time of each algorithm.Results:This research is tested through practical implementation based on the accuracy of each algorithm’s results and computational time to evaluate its performance in order to find a robust and reliable ND algorithm.Conclusions:The performance of the chosen ND algorithms is analyzed based on the results produced by this research approach. The time and accuracy of each algorithm are calculated and compared to suggest the best method.

scholarly journals Does BERT need domain adaptation for clinical negation detection?

2020 ◽  
Vol 27 (4) ◽  
pp. 584-591 ◽  
Author(s):  
Chen Lin ◽  
Steven Bethard ◽  
Dmitriy Dligach ◽  
Farig Sadeque ◽  
Guergana Savova ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Domain Adaptation ◽  
Fine Tuning ◽  
Adaptation Algorithm ◽  
Learning Methods ◽  
Clinical Text ◽  
Unsupervised Domain Adaptation ◽  
Adversarial Training ◽  
Negation Detection ◽  
Adaptation Method

Abstract Introduction Classifying whether concepts in an unstructured clinical text are negated is an important unsolved task. New domain adaptation and transfer learning methods can potentially address this issue. Objective We examine neural unsupervised domain adaptation methods, introducing a novel combination of domain adaptation with transformer-based transfer learning methods to improve negation detection. We also want to better understand the interaction between the widely used bidirectional encoder representations from transformers (BERT) system and domain adaptation methods. Materials and Methods We use 4 clinical text datasets that are annotated with negation status. We evaluate a neural unsupervised domain adaptation algorithm and BERT, a transformer-based model that is pretrained on massive general text datasets. We develop an extension to BERT that uses domain adversarial training, a neural domain adaptation method that adds an objective to the negation task, that the classifier should not be able to distinguish between instances from 2 different domains. Results The domain adaptation methods we describe show positive results, but, on average, the best performance is obtained by plain BERT (without the extension). We provide evidence that the gains from BERT are likely not additive with the gains from domain adaptation. Discussion Our results suggest that, at least for the task of clinical negation detection, BERT subsumes domain adaptation, implying that BERT is already learning very general representations of negation phenomena such that fine-tuning even on a specific corpus does not lead to much overfitting. Conclusion Despite being trained on nonclinical text, the large training sets of models like BERT lead to large gains in performance for the clinical negation detection task.

scholarly journals Joint Entity Extraction and Assertion Detection for Clinical Text

2019 ◽  
Author(s):  
Parminder Bhatia ◽  
Busra Celikkaya ◽  
Mohammed Khalilia
Keyword(s):  
Entity Extraction ◽  
Clinical Text

scholarly journals Variational Deep Logic Network for Joint Inference of Entities and Relations

2021 ◽  
pp. 1-38
Author(s):  
Wenya Wang ◽  
Sinno Jialin Pan
Keyword(s):  
Deep Learning ◽  
Representation Learning ◽  
Event Extraction ◽  
Relational Reasoning ◽  
Entity Extraction ◽  
Learning Models ◽  
Joint Inference ◽  
Logic Network ◽  
End To End ◽  
Relation Prediction

Abstract Nowadays, deep learning models have been widely adopted and achieved promising results on various application domains. Despite of their intriguing performance, most deep learning models function as black-boxes, lacking explicit reasoning capabilities and explanations, which are usually essential for complex problems. Take joint inference in information extraction as an example. This task requires the identification of multiple structured knowledge from texts, which is inter-correlated, including entities, events and the relationships between them. Various deep neural networks have been proposed to jointly perform entity extraction and relation prediction, which only propagate information implicitly via representation learning. However, they fail to encode the intensive correlations between entity types and relations to enforce their co-existence. On the other hand, some approaches adopt rules to explicitly constrain certain relational facts. However, the separation of rules with representation learning usually restrains the approaches with error propagation. Moreover, the pre-defined rules are inflexible and might bring negative effects when data is noisy. To address these limitations, we propose a variational deep logic network that incorporates both representation learning and relational reasoning via the variational EM algorithm. The model consists of a deep neural network to learn high-level features with implicit interactions via the self-attention mechanism and a relational logic network to explicitly exploit target interactions. These two components are trained interactively to bring the best of both worlds. We conduct extensive experiments ranging from fine-grained sentiment terms extraction, end-to-end relation prediction to end-to-end event extraction to demonstrate the effectiveness of our proposed method.

scholarly journals Extending NegEx with Kernel Methods for Negation Detection in Clinical Text

2015 ◽  
Author(s):  
Chaitanya Shivade ◽  
Marie-Catherine de Marneffe ◽  
Eric Fosler-Lussier ◽  
Albert M. Lai
Keyword(s):  
Kernel Methods ◽  
Clinical Text ◽  
Negation Detection

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

2015 ◽  
Vol 54 ◽  
pp. 213-219 ◽  
Author(s):  
Saeed Mehrabi ◽  
Anand Krishnan ◽  
Sunghwan Sohn ◽  
Alexandra M. Roch ◽  
Heidi Schmidt ◽  
...  
Keyword(s):  
Detection System ◽  
Clinical Text ◽  
Dependency Relation ◽  
Negation Detection

scholarly journals A fast, accurate, and generalisable heuristic-based negation detection algorithm for clinical text

2021 ◽  
Vol 130 ◽  
pp. 104216
Author(s):  
Luke T. Slater ◽  
William Bradlow ◽  
Dino FA. Motti ◽  
Robert Hoehndorf ◽  
Simon Ball ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Clinical Text ◽  
Negation Detection

scholarly journals An End-to-end Environment for Research Question-Driven Entity Extraction and Network Analysis

2017 ◽  
Author(s):  
Andre Blessing ◽  
Nora Echelmeyer ◽  
Markus John ◽  
Nils Reiter
Keyword(s):  
Network Analysis ◽  
Research Question ◽  
Entity Extraction ◽  
End To End

scholarly journals A fast, accurate, and generalisable heuristic-based negation detection algorithm for clinical text

2020 ◽  
Author(s):  
Luke T Slater ◽  
William Bradlow ◽  
Dino FA Motti ◽  
Robert Hoehndorf ◽  
Simon Ball ◽  
...  
Keyword(s):  
Text Mining ◽  
Gold Standard ◽  
Detection Algorithm ◽  
Heuristic Approach ◽  
University Hospitals ◽  
Rule Based ◽  
Clinical Text ◽  
Dependency Graphs ◽  
Negation Detection ◽  
Mimic Iii

AbstractBackgroundNegation detection is an important task in biomedical text mining. Particularly in clinical settings, it is of critical importance to determine whether findings mentioned in text are present or absent. Rule-based negation detection algorithms are a common approach to the task, and more recent investigations have resulted in the development of rule-based systems utilising the rich grammatical information afforded by typed dependency graphs. However, interacting with these complex representations inevitably necessitates complex rules, which are time-consuming to develop and do not generalise well. We hypothesise that a heuristic approach to determining negation via dependency graphs could offer a powerful alternative.ResultsWe describe and implement an algorithm for negation detection based on grammatical distance from a negatory construct in a typed dependency graph. To evaluate the algorithm, we develop two testing corpora comprised of sentences of clinical text extracted from the MIMIC-III database and documents related to hypertrophic cardiomyopathy patients routinely collected at University Hospitals Birmingham NHS trust. Gold-standard validation datasets were built by a combination of human annotation and examination of algorithm error. Finally, we compare the performance of our approach with four other rule-based algorithms on both gold-standard corpora.ConclusionsThe presented algorithm exhibits the best performance by f-measure over the MIMIC-III dataset, and a similar performance to the syntactic negation detection systems over the HCM dataset. It is also the fastest of the dependency-based negation systems explored in this study. Our results show that while a single heuristic approach to dependency-based negation detection is ignorant to certain advanced cases, it nevertheless forms a powerful and stable method, requiring minimal training and adaptation between datasets. As such, it could present a drop-in replacement or augmentation for many-rule negation approaches in clinical text-mining pipelines, particularly for cases where adaptation and rule development is not required or possible.

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