CDE-IIITH at SemEval-2016 Task 12: Extraction of Temporal Information from Clinical documents using Machine Learning techniques

Chikka, Veera Raghavendra

doi:10.18653/v1/s16-1192

Cited by 21 publications

(10 citation statements)

References 11 publications

(8 reference statements)

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“…For the i2b2 2012 dataset, the authors of [73] used a single CRF classifier, even in a scenario with two DCTs (admission and discharge dates). For the Clinical TempEval shared tasks, a single CRF classifier was used by the authors of [28,84,85,91,111,112,128].…”

Section: Machine Learningmentioning

confidence: 99%

Temporal Relation Extraction in Clinical Texts

et al. 2021

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Unstructured data in electronic health records, represented by clinical texts, are a vast source of healthcare information because they describe a patient's journey, including clinical findings, procedures, and information about the continuity of care. The publication of several studies on temporal relation extraction from clinical texts during the last decade and the realization of multiple shared tasks highlight the importance of this research theme. Therefore, we propose a review of temporal relation extraction in clinical texts. We analyzed 105 articles and verified that relations between events and document creation time, a coarse temporality type, were addressed with traditional machine learning–based models with few recent initiatives to push the state-of-the-art with deep learning–based models. For temporal relations between entities (event and temporal expressions) in the document, factors such as dataset imbalance because of candidate pair generation and task complexity directly affect the system's performance. The state-of-the-art resides on attention-based models, with contextualized word representations being fine-tuned for temporal relation extraction. However, further experiments and advances in the research topic are required until real-time clinical domain applications are released. Furthermore, most of the publications mainly reside on the same dataset, hindering the need for new annotation projects that provide datasets for different medical specialties, clinical text types, and even languages.

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Section: Machine Learningmentioning

confidence: 99%

Temporal Relation Extraction in Clinical Texts

et al. 2021

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“…In fact, the best performance was achieved by the UTHealth team (Lee et al 2016) using an end-to-end system based on a linear and structural Hidden Markov Model (HMM)-SVM. Only a few teams tried a neural based method, including recurrent neural networks-based (RNN) models (Fries 2016) and convolutional neural networks-based (CNN) models (Chikka 2016;Li and Huang 2016). Furthermore, among those teams, only Chikka (2016) participated in the contains identification task, being around 0.30 below UTHealth's top performance.…”

Section: Previous Workmentioning

confidence: 99%

“…Only a few teams tried a neural based method, including recurrent neural networks-based (RNN) models (Fries 2016) and convolutional neural networks-based (CNN) models (Chikka 2016;Li and Huang 2016). Furthermore, among those teams, only Chikka (2016) participated in the contains identification task, being around 0.30 below UTHealth's top performance. Lin et al (2016), Dligach, Miller, Lin, Bethard, and Savova (2017) and Leeuwenberg and Moens (2017) followed the settings of Clinical TempEval 2016 but they did not participate in the competition.…”

Section: Previous Workmentioning

confidence: 99%

Empirical Exploration of the Challenges in Temporal Relation Extraction from Clinical Text

Galvan-Sosa

Matsuda

Okazaki

et al. 2020

Journal of Natural Language Processing

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Time is an important concept in human-cognition, fundamental to a wide range of reasoning tasks in the clinical domain. Results of the Clinical TempEval 2016 challenge, a set of shared tasks that evaluate temporal information extraction systems in the clinical domain, indicate that current state-of-the-art systems do well in solving event and time expression identification but perform poorly in temporal relation extraction. This study aims to identify and analyze the reason(s) for this uneven performance. It adapts a general domain tree-based bidirectional long short-term memory recurrent neural network model for semantic relation extraction to the task of temporal relation extraction in the clinical domain, and tests the system in a binary and multi-class classification setting by experimenting with general and in-domain word embeddings. Its results outperform the best Clinical TempEval 2016 system and the current stateof-the-art model. However, there is still a significant gap between the system and human performance. Consequently, this study delivers a deep analysis of the results, identifying a high incidence of nouns as events and class overlapping as posing major challenges in this task.

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“…However, data in these challenges are relatively hard to acquire, and therefore they are not used in this paper. As in the news data, traditional machine learning approaches (Lee et al, 2016;Chikka, 2016;Xu et al, 2013;Tang et al, 2013;Savova et al, 2010) that tackle the end-to-end event and temporal relation extraction problem require timeconsuming feature engineering such as collecting lexical and syntax features. Some recent work (Dligach et al, 2017;Leeuwenberg and Moens, 2017;Galvan et al, 2018) apply neural network-based methods to model the temporal relations, but are not capable of incorporating prior knowledge about clinical events and temporal relations as proposed by our framework.…”

Section: Related Workmentioning

confidence: 99%

Deep Structured Neural Network for Event Temporal Relation Extraction

Han¹,

Hsu²,

Yang³

et al. 2019

Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

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We propose a novel deep structured learning framework for event temporal relation extraction. The model consists of 1) a recurrent neural network (RNN) to learn scoring functions for pair-wise relations, and 2) a structured support vector machine (SSVM) to make joint predictions. The neural network automatically learns representations that account for long-term contexts to provide robust features for the structured model, while the SSVM incorporates domain knowledge such as transitive closure of temporal relations as constraints to make better globally consistent decisions. By jointly training the two components, our model combines the benefits of both data-driven learning and knowledge exploitation. Experimental results on three highquality event temporal relation datasets (TCR, MATRES, and TB-Dense) demonstrate that incorporated with pre-trained contextualized embeddings, the proposed model achieves significantly better performances than the stateof-the-art methods on all three datasets. We also provide thorough ablation studies to investigate our model.

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CDE-IIITH at SemEval-2016 Task 12: Extraction of Temporal Information from Clinical documents using Machine Learning techniques

Cited by 21 publications

References 11 publications

Temporal Relation Extraction in Clinical Texts

Temporal Relation Extraction in Clinical Texts

Empirical Exploration of the Challenges in Temporal Relation Extraction from Clinical Text

Deep Structured Neural Network for Event Temporal Relation Extraction

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