Knowledge-rich temporal relation identification and classification in clinical notes

D’Souza, Jennifer; Ng, Vincent

doi:10.1093/database/bau109

Cited by 11 publications

(13 citation statements)

References 14 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors of [11] considered all consecutive pairs in a sentence or pairs with a dependency relation. The authors of [73] and [74] used the strategy proposed in [11]. Both strategies were successful, with [11] being more restrictive in terms of the number of created pairs.…”

Section: Hybrid Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Temporal Relation Extraction in Clinical Texts

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Hybrid Systemsmentioning

confidence: 99%

“…The authors of [11] also focused on the main events, considering pairs involving all first and last events in two consecutive sentences. The authors of [73] and [74] also used the strategy found in [11].…”

Section: Hybrid Systemsmentioning

confidence: 99%

Temporal Relation Extraction in Clinical Texts

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In other studies, NLP has been used to model the corpus to extract characteristics that define clinical experiments. This approximation has been described as a hybrid according to the authors of [29], employing NLP to extract six types of features, such as a characteristic of pairs, dependence relations, lexical relations, WordNet relations, predicated argument and discourse relations, performed according to standard criteria. In the classification process, an F1-score of 0.61 was obtained with a support vector machine (SVM).…”

Section: Related Workmentioning

confidence: 99%

Application of Machine Learning and Word Embeddings in the Classification of Cancer Diagnosis Using Patient Anamnesis

et al. 2020

View full text Add to dashboard Cite

Currently, one of the main challenges for information systems in healthcare is focused on support for health professionals regarding disease classifications. This work presents an innovative method for a recommendation system for the diagnosis of breast cancer using patient medical histories. In this proposal, techniques of natural language processing (NLP) were implemented on real datasets: one comprised 160, 560 medical histories of anonymous patients from a hospital in Chile for the following categories: breast cancer, cysts and nodules, other cancer, breast cancer surgeries and other diagnoses; and the other dataset was obtained from the MIMIC III dataset. With the application of word-embedding techniques, such as word2vec's skip-gram and BERT, and machine learning techniques, a recommendation system as a tool to support the physician's decision-making was implemented. The obtained results demonstrate that using word embeddings can define a good-quality recommendation system. The results of 20 experiments with 5-fold cross-validation for anamnesis written in Spanish yielded an F1 of 0.980 ± 0.0014 on the classification of 'cancer' versus 'not cancer' and 0.986 ± 0.0014 for 'breast cancer' versus 'other cancer'. Similar results were obtained with the MIMIC III dataset.

show abstract

“…The authors used semantic and discourse relations and a combination between machine learning and rule based systems. In another work, D’Souza et al [109], have identified and classified temporal relations to 12 relation types rather than focusing on ‘three’ temporal relations as in the shared task, the experiments on the i2b2 corpus showed the effectiveness of the approach over the state-of-the-art 3-class classification results reported in the 2012 i2b2 challenge. The temporal relation extraction is also the process of identifying the chronological order of entities.…”

Section: Extraction Of Temporal Informationmentioning

confidence: 99%

Temporal data representation, normalization, extraction, and reasoning: A review from clinical domain

Madkour

Benhaddou

Tao

2016

Computer Methods and Programs in Biomedicine

View full text Add to dashboard Cite

Background and Objective We live our lives by the calendar and the clock, but time is also an abstraction, even an illusion. The sense of time can be both domain-specific and complex, and is often left implicit, requiring significant domain knowledge to accurately recognize and harness. In the clinical domain, the momentum gained from recent advances in infrastructure and governance practices has enabled the collection of tremendous amount of data at each moment in time. Electronic Health Records (EHRs) have paved the way to making these data available for practitioners and researchers. However, temporal data representation, normalization, extraction and reasoning are very important in order to mine such massive data and therefore for constructing the clinical timeline. The objective of this work is to provide an overview of the problem of constructing a timeline at the clinical point of care and to summarize the state-of-the-art in processing temporal information of clinical narratives. Methods This review surveys the methods used in three important area: modeling and representing of time, Medical NLP methods for extracting time, and methods of time reasoning and processing. The review emphasis on the current existing gap between present methods and the semantic web technologies and catch up with the possible combinations. Results the main findings of this review is revealing the importance of time processing not only in constructing timelines and clinical decision support systems but also as a vital component of EHR data models and operations. Conclusions Extracting temporal information in clinical narratives is a challenging task. The inclusion of ontologies and semantic web will lead to better assessment of the annotation task and, together with medical NLP techniques, will help resolving granularity and co-reference resolution problems.

show abstract

Knowledge-rich temporal relation identification and classification in clinical notes

Cited by 11 publications

References 14 publications

Temporal Relation Extraction in Clinical Texts

Temporal Relation Extraction in Clinical Texts

Application of Machine Learning and Word Embeddings in the Classification of Cancer Diagnosis Using Patient Anamnesis

Temporal data representation, normalization, extraction, and reasoning: A review from clinical domain

Contact Info

Product

Resources

About