Temporal Annotation in the Clinical Domain

Styler, William F.; Bethard, Steven; Finan, Sean; Palmer, Martha; Pradhan, S. Sandeep; Groen, Piet C. de; Erickson, Bradley J.; Miller, Timothy A.; Lin, Chen; Savova, Guergana; Pustejovsky, James

doi:10.1162/tacl_a_00172

Cited by 150 publications

(119 citation statements)

References 22 publications

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“…Therefore TimeML has been the base for many other annotation schemas. The THYME-TimeML [69] is an annotation guideline that is developed to create robust gold standards for semantic information in clinical notes. A simplified version of this guideline formed the basis for the 2012 Informatics for Integrating Biology and the Bedside (i2b2) medical-domain temporal relation 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

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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.

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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

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“…Other notable collections of medical records include the THYME corpus, a collection of over 1,200 de-identified notes from the Mayo Clinic, representing patients from the oncology department, specifically those with brain or colon cancer (Styler et al, 2014); a recently created corpus of 3,503 de-identified medical records of 22 different types, including discharge summaries, progress notes, and referrals (Deleger et al, 2014); and TREC Medical Records corpora (Vorhees and Hersh, 2012).…”

Section: Related Workmentioning

confidence: 99%

Creation of a new longitudinal corpus of clinical narratives

Kumar

Stubbs

Shaw

et al. 2015

Journal of Biomedical Informatics

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The 2014 i2b2/UTHealth Natural Language Processing (NLP) shared task featured a new longitudinal corpus of 1,304 records representing 296 diabetic patients. The corpus contains three cohorts: patients who have a diagnosis of coronary artery disease (CAD) in their first record, and continue to have it in subsequent records; patients who do not have a diagnosis of CAD in the first record, but develop it by the last record; patients who do not have a diagnosis of CAD in any record. This paper details the process used to select records for this corpus and provides an overview of novel research uses for this corpus. This corpus is the only annotated corpus of longitudinal clinical narratives currently available for research to the general research community.

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“…[5]–[7] Approaches used in the clinical domain include some of those mentioned above, including rule-based sieve approaches,[8], [9] traditional pairwise mention classification approaches,[10] and hypergraph factorization approaches. [11], [12] Before the recent release of shared datasets for training, machine learning approaches were uncommon.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, this work was motivated by preliminary experiments that found the existing cTAKES coreference resolution system [17] performed poorly on a new gold standard coreference dataset that our lab and collaborators created as part of the THYME (Temporal History of Your Medical Events) project. [7] It is worth considering whether clinical coreference resolution is a meaningful task that warrants domain-specific research, or whether clinical text should just be considered a domain to adapt generally-trained coreference systems to. There are reasons to consider the problems separate.…”

Section: Introductionmentioning

confidence: 99%

Towards generalizable entity-centric clinical coreference resolution

Miller

Dligach

Bethard

et al. 2017

Journal of Biomedical Informatics

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Objective This work investigates the problem of clinical coreference resolution in a model that explicitly tracks entities, and aims to measure the performance of that model in both traditional in-domain train/test splits and cross-domain experiments that measure the generalizability of learned models. Methods The two methods we compare are a baseline mention-pair coreference system that operates over pairs of mentions with best-first conflict resolution and a mention-synchronous system that incrementally builds coreference chains. We develop new features that incorporate distributional semantics, discourse features, and entity attributes. We use two new coreference datasets with similar annotation guidelines – the THYME colon cancer dataset and the DeepPhe breast cancer dataset. Results The mention-synchronous system performs similarly on in-domain data but performs much better on new data. Part of speech tag features prove superior in feature generalizability experiments over other word representations. Our methods show generalization improvement but there is still a performance gap when testing in new domains. Discussion Generalizability of clinical NLP systems is important and under-studied, so future work should attempt to perform cross-domain and cross-institution evaluations and explicitly develop features and training regimens that favor generalizability. A performance-optimized version of the mention-synchronous system will be included in the open source Apache cTAKES software.

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Temporal Annotation in the Clinical Domain

Cited by 150 publications

References 22 publications

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

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

Creation of a new longitudinal corpus of clinical narratives

Towards generalizable entity-centric clinical coreference resolution

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