Classifying temporal relations in clinical data: A hybrid, knowledge-rich approach

D’Souza, Jennifer; Ng, Vincent

doi:10.1016/j.jbi.2013.08.003

Cited by 14 publications

(17 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…27 33 41 Other third party temporal expression taggers, SUTIME 42 and GUTIME, 30 were utilized by others. 38 Despite some success in utilizing existing systems, most teams developed their own frequency detection and normalization components.…”

Section: Event/timex3 Trackmentioning

confidence: 99%

Evaluating temporal relations in clinical text: 2012 i2b2 Challenge

Sun¹,

Rumshisky²,

Uzuner³

2013

J Am Med Inform Assoc

384

329

View full text Add to dashboard Cite

For event detection, statistical machine learning (ML) methods consistently showed superior performance. While ML and rule based methods seemed to detect temporal expressions equally well, the best systems overwhelmingly adopted a rule based approach for value normalization. For temporal relation classification, the systems using hybrid approaches that combined ML and heuristics based methods produced the best results.

show abstract

Section: Event/timex3 Trackmentioning

confidence: 99%

Evaluating temporal relations in clinical text: 2012 i2b2 Challenge

Sun¹,

Rumshisky²,

Uzuner³

2013

J Am Med Inform Assoc

384

329

View full text Add to dashboard Cite

show abstract

“…The study demonstrated that adding syntactic features results in a considerable improvement over the state-of-the-art methods of temporal relations classification. D’Souza et al [107] classified TLINKs using PropBank-style predicate-argument relations, and discourse relations. TLINK represents the temporal relation that holds between events, times or between an event and a time with different subsets of values (simultaneous, before, after, etc.)…”

Section: Extraction Of Temporal Informationmentioning

confidence: 99%

“…The 2012 Informatics for Integrating Biology and the Bedside (i2b2) challenge [121] marked a shift in the wide community in the sense that it refocused the research initiative towards temporal relation extraction from newswire data to data from the clinical domain [107]. Twenty teams representing 23 organizations and nine countries have participated in the medication challenge.…”

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

“…Most state-of-the-art systems presented for the 2012 i2b2 clinical NLP challenge used machine learning-based methods to extract relationships between events and DCT [23, 24]. For example, the best system proposed by Tang et al adopted SVMs [23].…”

Section: Related Workmentioning

confidence: 99%

An automatic system to identify heart disease risk factors in clinical texts over time

Chen

Tang

et al. 2015

Journal of Biomedical Informatics

View full text Add to dashboard Cite

Despite recent progress in prediction and prevention, heart disease remains a leading cause of death. One preliminary step in heart disease prediction and prevention is risk factor identification. Many studies have been proposed to identify risk factors associated with heart disease; however, none have attempted to identify all risk factors. In 2014, the National Center of Informatics for Integrating Biology and Beside (i2b2) issued a clinical natural language processing (NLP) challenge that involved a track (track 2) for identifying heart disease risk factors in clinical texts over time. This track aimed to identify medically relevant information related to heart disease risk and track the progression over sets of longitudinal patient medical records. Identification of tags and attributes associated with disease presence and progression, risk factors, and medications in patient medical history were required. Our participation led to development of a hybrid pipeline system based on both machine learning-based and rule-based approaches. Evaluation using the challenge corpus revealed that our system achieved an F1-score of 92.68%, making it the top-ranked system (without additional annotations) of the 2014 i2b2 clinical NLP challenge.

show abstract

Classifying temporal relations in clinical data: A hybrid, knowledge-rich approach

Cited by 14 publications

References 15 publications

Evaluating temporal relations in clinical text: 2012 i2b2 Challenge

Evaluating temporal relations in clinical text: 2012 i2b2 Challenge

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

An automatic system to identify heart disease risk factors in clinical texts over time

Contact Info

Product

Resources

About