ctakes temporal

ctakes-temporal

This module is dedicated to processing electronic medical records for meaningful events, temporal expressions, and their relations on a timeline.

Temporal relations are of prime importance in biomedicine as they are intrinsically linked to diseases, signs and symptoms, and treatments. The identification of temporal relations in medical text has drawn growing attention because of its potential to dramatically increase the understanding of many medical phenomena such as disease progression, longitudinal effects of medications, and a patient's clinical course, and its many clinical applications such as question answering[1, 2], clinical outcomes prediction[3], and the recognition of temporal patterns and timelines[4, 5].

Key components

Event annotator
A Begin-Inside-Outside (BIO) style sequence annotator for clinically meaningful events, i.e. anything that would show up on a detailed timeline of the patient’s care or life.

Temporal expression annotators
A series of BIO style sequence annotators that employed forward and backward search algorithms and multiple learning methods (Support Vector Machine (SVM), Conditional Random Field (CRV) ) for annotating temporal expressions which would provide concrete temporal references throughout the document or section, e.g. “today”, “24 hours ago”, “postoperative”. More details can be found in [6].

DocTimeRel annotator
For every event, there is an SVM-based annotator that can automatically reason the temporal relation between the target event and the document creation time (DCT). This module provided a basic and stable temporal solution that can position all events into coarse temporal bins, e.g. “before the DCT”, “after the DCT”, or “overlap the DCT”. This annotator has proved helpful in solving real clinical temporal-sensitive tasks for multiple institutions [5].

Temporal relation (TLINK) annotators
SVM-based annotators for detecting within-sentence Event-Time relations and Event-Event relations. For i2b2 datasets there are also cross sentence Event-Time and Event-Event relation annotators. Multiple techniques have been implemented, including narrative container-based annotation concept [7], tree kernels [8] for syntactic similarity measurement, multi-layered temporal modeling [9], event expansion [10], and deep neural network methods [11, 12].

Evaluation

The SVM-based temporal relation annotators achieve an F-score of 0.589 which outperform the best system of Clinical TempEval 2016 [13], whose F-score was 0.573. State-of-the-art results for event-time relations have been achieved with our neural network approaches.

All the above annotators were trained and tested on colon cancer notes from the THYME data set [14].

References

1. Das, A.K. and M.A. Musen. A comparison of the temporal expressiveness of three database query methods. in Annual Symposium on Computer Applications in Medical Care. 1995. IEEE COMPUTER SOCIETY PRESS. 2. Kahn, M.G., L.M. Fagan, and S. Tu, Extensions to the time-oriented database model to support temporal reasoning in medical expert systems. Methods of information in medicine, 1990. 30(1): p. 4-14. 3. Schmidt, R., S. Ropele, C. Enzinger, et al., White matter lesion progression, brain atrophy, and cognitive decline: the Austrian stroke prevention study. Annals of neurology, 2005. 58(4): p. 610-616. 4. Zhou, L. and G. Hripcsak, Temporal reasoning with medical data—a review with emphasis on medical natural language processing. Journal of biomedical informatics, 2007. 40(2): p. 183-202. 5. Lin, C., E.W. Karlson, D. Dligach, et al., Automatic identification of methotrexate-induced liver toxicity in patients with rheumatoid arthritis from the electronic medical record. Journal of the American Medical Informatics Association, 2014: p. amiajnl-2014-002642. 6. Miller, T.A., S. Bethard, D. Dligach, et al., Extracting Time Expressions from Clinical Text, in Proceedings of BioNLP 15. 2015. 7. Miller, T.A., S. Bethard, D. Dligach, et al., Discovering narrative containers in clinical text, in ACL 2013. 2013: Sofia, Bulgaria. p. 18. 8. Lin, C., T. Miller, A. Kho, et al., Descending-Path Convolution Kernel for Syntactic Structures, in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL). 2014: Baltimore, Maryland, USA. p. 81-86. 9. Lin, C., D. Dligach, T.A. Miller, et al., Multilayered temporal modeling for the clinical domain. J Am Med Inform Assoc, 2016. 23(2): p. 387-95. 10. Lin, C., T. Miller, D. Dligach, et al., Improving Temporal Relation Extraction with Training Instance Augmentation. BioNLP 2016, 2016: p. 108. 11. Dligach, D., T. Miller, C. Lin, et al., Neural temporal relation extraction., in European Chapter of the Association for Computational Linguistics (EACL 2017). 2017: Valencia, Spain. 12. Hartzell, E. and C. Lin. Enhancing Clinical Temporal Relation Discovery with Syntactic Embeddings from GloVe. in International Conference on Intelligent Biology and Medicine (ICIBM 2016). 2016. Houston, Texas, USA. 13. Bethard, S., G. Savova, W.-T. Chen, et al., Semeval-2016 task 12: Clinical tempeval. Proceedings of SemEval, 2016: p. 1052-1062. 14. Styler IV, W.F., S. Bethard, S. Finan, et al., Temporal annotation in the clinical domain. Transactions of the Association for Computational Linguistics, 2014. 2: p. 143-154.

Piper Files

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Piper Files

Default Relation Temporal Pipeline

Clinical Pipeline with degree-of, location-of, events, times, temporal and event-doc creation time relations.

Default Relation Temporal Pipeline

$\textcolor{gray}{\textsf{// Clinical Pipeline with degree-of, location-of, events, times, temporal and event-doc creation time relations. }}$

$\textcolor{gray}{\textsf{// Clinical Pipeline with degree-of and location-of relations. }}$
$\textcolor{magenta}{\textsf{load}}$ DefaultRelationPipeline

$\textcolor{gray}{\textsf{// events, times, event-event, event-time and event-doc creation time relations. }}$
$\textcolor{magenta}{\textsf{load}}$ TemporalSubPipe

Default Temporal Pipeline

Clinical Pipeline with events, times, event-event and event-time relations plus event-document creation time relations.

Default Temporal Pipeline

$\textcolor{gray}{\textsf{// Clinical Pipeline with events, times, event-event and event-time relations plus event-document creation time relations. }}$

$\textcolor{gray}{\textsf{// Clinical Pipeline. }}$
$\textcolor{magenta}{\textsf{load}}$ DefaultFastPipeline

$\textcolor{gray}{\textsf{// events, times, event-event, event-time and event-doc creation time relations. }}$
$\textcolor{magenta}{\textsf{load}}$ TemporalSubPipe

Sectioned Relation Temporal Pipeline

Clinical Pipeline with sections, paragraphs, lists, degree-of, location-of, events, times, temporal and event-doc creation time relations.

Sectioned Relation Temporal Pipeline

$\textcolor{gray}{\textsf{// Clinical Pipeline with sections, paragraphs, lists, degree-of, location-of, events, times, temporal and event-doc creation time relations. }}$

$\textcolor{gray}{\textsf{// Clinical Pipeline with section, paragraph and list detection with degree-of and location-of relations. }}$
$\textcolor{magenta}{\textsf{load}}$ SectionedRelationPipeline

$\textcolor{gray}{\textsf{// events, times, event-event, event-time and event-doc creation time relations. }}$
$\textcolor{magenta}{\textsf{load}}$ TemporalSubPipe

Sectioned Temporal Pipeline

Clinical Pipeline with sections, paragraphs, lists, events, times, temporal and event-doc creation time relations.

Sectioned Temporal Pipeline

$\textcolor{gray}{\textsf{// Clinical Pipeline with sections, paragraphs, lists, events, times, temporal and event-doc creation time relations. }}$

$\textcolor{gray}{\textsf{// Clinical Pipeline with section, paragraph and list detection. }}$
$\textcolor{magenta}{\textsf{load}}$ SectionedFastPipeline

$\textcolor{gray}{\textsf{// events, times, event-event, event-time and event-doc creation time relations. }}$
$\textcolor{magenta}{\textsf{load}}$ TemporalSubPipe

Temporal Sub Pipe

Commands and parameters to create a temporal processing sub-pipeline.

Temporal Sub Pipe

$\textcolor{gray}{\textsf{// Commands and parameters to create a temporal processing sub-pipeline. }}$
$\textcolor{gray}{\textsf{// This is not a full pipeline. }}$

$\textcolor{gray}{\textsf{// 'Generic' Events. Use addDescription and let the EventAnnotator set itself up with defaults. }}$
$\textcolor{green}{\textsf{addDescription}}$ $\textcolor{blue}{\textsf{EventAnnotator}}$

$\textcolor{gray}{\textsf{// Times. Use addLogged to log start and finish of processing. There aren't default models, so set specifically }}$
$\textcolor{green}{\textsf{add}}$ $\textcolor{blue}{\textsf{BackwardsTimeAnnotator}}$ $\textcolor{purple}{\textsf{classifierJarPath}}$= $\textcolor{violet}{\textsf{/org/apache/ctakes/temporal/models/timeannotator/model.jar}}$

$\textcolor{gray}{\textsf{// DocTimeRel: the relation bin for Events to the Document Creation Time. }}$
$\textcolor{green}{\textsf{add}}$ $\textcolor{blue}{\textsf{DocTimeRelAnnotator}}$ $\textcolor{purple}{\textsf{classifierJarPath}}$= $\textcolor{violet}{\textsf{/org/apache/ctakes/temporal/models/doctimerel/model.jar}}$

$\textcolor{gray}{\textsf{// Event - Time binary relations. }}$
$\textcolor{green}{\textsf{add}}$ $\textcolor{blue}{\textsf{EventTimeRelationAnnotator}}$ $\textcolor{purple}{\textsf{classifierJarPath}}$= $\textcolor{violet}{\textsf{/org/apache/ctakes/temporal/models/eventtime/model.jar}}$

$\textcolor{gray}{\textsf{// Event - Event binary relations. }}$
$\textcolor{green}{\textsf{add}}$ $\textcolor{blue}{\textsf{EventEventRelationAnnotator}}$ $\textcolor{purple}{\textsf{classifierJarPath}}$= $\textcolor{violet}{\textsf{/org/apache/ctakes/temporal/models/eventevent/model.jar}}$

$\textcolor{gray}{\textsf{// Need to improve. Generic Events (head words) should be replaced by overlapping subclasses with relations adjusted. }}$
$\textcolor{gray}{\textsf{//add FullTemporalExtractionPipeline.CopyPropertiesToTemporalEventAnnotator }}$

Ts Default Relation Temporal Pipeline

Thread Safe Clinical Pipeline with degree-of and location-of relations, events, times, temporal abd event-doc creation time relations.

Ts Default Relation Temporal Pipeline

$\textcolor{gray}{\textsf{// Thread Safe Clinical Pipeline with degree-of and location-of relations, events, times, temporal abd event-doc creation time relations. }}$

$\textcolor{gray}{\textsf{// Thread safe Clinical Pipeline with degree-of and location-of relations. }}$
$\textcolor{magenta}{\textsf{load}}$ TsDefaultRelationPipeline

$\textcolor{gray}{\textsf{// Thread safe events, times, event-event, event-time and event-doc creation time relations. }}$
$\textcolor{magenta}{\textsf{load}}$ TsTemporalSubPipe

Ts Default Temporal Pipeline

Thread Safe Default Clinical Pipeline with events, times, event-event and event-time relations plus event-document creation time relations

Ts Default Temporal Pipeline

$\textcolor{gray}{\textsf{// Thread Safe Default Clinical Pipeline with events, times, event-event and event-time relations plus event-document creation time relations }}$

$\textcolor{gray}{\textsf{// Thread safe Clinical Pipeline. }}$
$\textcolor{magenta}{\textsf{load}}$ TsDefaultFastPipeline

$\textcolor{gray}{\textsf{// Thread safe events, times, event-event, event-time and event-doc creation time relations. }}$
$\textcolor{magenta}{\textsf{load}}$ TsTemporalSubPipe

Ts Sectioned Relation Temporal Pipeline

Thread safe Clinical Pipeline with sections, paragraphs, lists, degree-of, location-of, events, times, temporal and event-doc creation time relations.

Ts Sectioned Relation Temporal Pipeline

$\textcolor{gray}{\textsf{// Thread safe Clinical Pipeline with sections, paragraphs, lists, degree-of, location-of, events, times, temporal and event-doc creation time relations. }}$


$\textcolor{gray}{\textsf{// Clinical Pipeline with section, paragraph and list detection with degree-of and location-of relations. }}$
$\textcolor{magenta}{\textsf{load}}$ TsSectionedRelationPipeline

$\textcolor{gray}{\textsf{// Thread safe events, times, event-event, event-time and event-doc creation time relations. }}$
$\textcolor{magenta}{\textsf{load}}$ TsTemporalSubPipe

Ts Sectioned Temporal Pipeline

Thread safe Clinical Pipeline with sections, paragraphs, lists, events, times, temporal and event-doc creation time relations.

Ts Sectioned Temporal Pipeline

$\textcolor{gray}{\textsf{// Thread safe Clinical Pipeline with sections, paragraphs, lists, events, times, temporal and event-doc creation time relations. }}$


$\textcolor{gray}{\textsf{// Clinical Pipeline with section, paragraph and list detection. }}$
$\textcolor{magenta}{\textsf{load}}$ TsSectionedFastPipeline

$\textcolor{gray}{\textsf{// Thread safe events, times, event-event, event-time and event-doc creation time relations. }}$
$\textcolor{magenta}{\textsf{load}}$ TsTemporalSubPipe

Ts Temporal Sub Pipe

Commands and parameters to create a thread safe temporal processing sub-pipeline.

Ts Temporal Sub Pipe

$\textcolor{gray}{\textsf{// Commands and parameters to create a thread safe temporal processing sub-pipeline. }}$
$\textcolor{gray}{\textsf{// This is not a full pipeline. }}$

$\textcolor{gray}{\textsf{// 'Generic' Events. Use addDescription and let the EventAnnotator set itself up with defaults. }}$
$\textcolor{green}{\textsf{addDescription}}$ $\textcolor{blue}{\textsf{concurrent.ThreadSafeEventAnnotator}}$

$\textcolor{gray}{\textsf{// Times. Use addLogged to log start and finish of processing. There aren't default models, so set specifically }}$
$\textcolor{green}{\textsf{add}}$ $\textcolor{blue}{\textsf{concurrent.ThreadSafeBackTimeAnnotator}}$ $\textcolor{purple}{\textsf{classifierJarPath}}$= $\textcolor{violet}{\textsf{/org/apache/ctakes/temporal/models/timeannotator/model.jar}}$

$\textcolor{gray}{\textsf{// DocTimeRel: the relation bin for Events to the Document Creation Time. }}$
$\textcolor{green}{\textsf{add}}$ $\textcolor{blue}{\textsf{concurrent.ThreadSafeDocTimeRelAnnotator}}$ $\textcolor{purple}{\textsf{classifierJarPath}}$= $\textcolor{violet}{\textsf{/org/apache/ctakes/temporal/models/doctimerel/model.jar}}$

$\textcolor{gray}{\textsf{// Event - Time binary relations. }}$
$\textcolor{green}{\textsf{add}}$ $\textcolor{blue}{\textsf{concurrent.ThreadSafeEventTimeRelAnnotator}}$ $\textcolor{purple}{\textsf{classifierJarPath}}$= $\textcolor{violet}{\textsf{/org/apache/ctakes/temporal/models/eventtime/model.jar}}$

$\textcolor{gray}{\textsf{// Event - Event binary relations. }}$
$\textcolor{green}{\textsf{add}}$ $\textcolor{blue}{\textsf{concurrent.ThreadSafeEventEventRelAnnotator}}$ $\textcolor{purple}{\textsf{classifierJarPath}}$= $\textcolor{violet}{\textsf{/org/apache/ctakes/temporal/models/eventevent/model.jar}}$

$\textcolor{gray}{\textsf{// Need to improve. Generic Events (head words) should be replaced by overlapping subclasses with relations adjusted. }}$
$\textcolor{gray}{\textsf{//add FullTemporalExtractionPipeline.CopyPropertiesToTemporalEventAnnotator }}$