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(Who We Are)
Line 25: Line 25:
** Jim Martin
** Jim Martin
** Wayne Ward
** Wayne Ward
** Steven Bethard
** Steven Bethard (at University of Alabama at Burmingham as of Sept, 2013)
** William Styler
** William Styler
** Arrick Lanfranchi (through August, 2012)
** Arrick Lanfranchi (through August, 2012)

Revision as of 20:03, 20 December 2013


Welcome to the THYME project

Welcome to the Temporal Histories of Your Medical Event (THYME) project (THYME is pronounced [taim]).

The overarching long-term vision of our research is to create novel technologies for processing clinical free text. Such technologies will enable sophisticated and efficient indexing, retrieval and data mining over the ever increasing amounts of electronic clinical data. Processing free text poses a number of challenges to which the fields of Artificial intelligence, natural language processing and computer science in general have made advances. Methods for processing free text are informed by linguistic theory combined with the power of statistical inferencing. A key component to the next step, natural language understanding, is discovering events 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. Understanding the timeline of clinically relevant events is key to the next generation of translational research where the importance of generalizing over large amounts of data holds the promise of deciphering biomedical puzzles.

The goal of our current proposal is to discover temporal relations from clinical free text through achieving four specific aims:

Specific Aim 1: Develop (1) a temporal relation annotation schema and guidelines for clinical free text based on TimeML, which will require extensions to Treebank, PropBank and VerbNet annotation guidelines to the clinical domain, (2) an annotated corpus (500K words of clinical narrative) following the temporal relations schema with additions to Treebank, PropBank and VerbNet, (3) a descriptive study comparing temporal relations in the clinical and general domains.

Specific Aim 2: Extend and evaluate existing methods and/or develop new algorithms for temporal relation discovery in the clinical domain. Component-level evaluation

Specific Aim 3: Integrate best method and/or a variety of methods for temporal relation discovery into Apache cTAKES ( and release as open source annotators in the pipeline. Functional testing. Dissemination activities.

Specific Aim 4: System-level evaluation. Test the functionality of the enhanced Apache cTAKES ( on translational research use cases, e.g. the progression of colon cancer as documented in clinical notes and pathology reports, the progression of brain tumor as documented in radiology reports.

The methods we will use for the temporal relation discovery are based on machine learning, e.g., Support Vector Machine technology. Such methods require the annotation of a reference standard from which the computations are derived. The best methods will be released as part of the cTAKES for the larger community to use and contribute to. We will test the methods against biomedical queries.

ACKNOWLEDGMENT: The project described is supported by Grant Number R01LM010090 from the National Library Of Medicine. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Library Of Medicine or the National Institutes of Health.

The project period is October, 2010 - September, 2014.

Who We Are

  • University of Colorado
    • Martha Palmer (PI)
    • Jim Martin
    • Wayne Ward
    • Steven Bethard (at University of Alabama at Burmingham as of Sept, 2013)
    • William Styler
    • Arrick Lanfranchi (through August, 2012)
    • Tim O'Gorman
    • Kevin Crooks
    • and several Lingustics and Computer Science graduate students
  • Boston Childrens Hospital/Harvard Medical School
    • Guergana Savova (PI)
    • Dmitriy Dligach
    • Timothy Miller
    • Sameer Pradhan
    • Sean Finan
    • Chen Lin
    • David Harris
    • Jennifer Green (through December, 2013)
  • Mayo Clinic
    • Piet de Groen
    • Brad Erickson
    • James Masanz
    • Donna Ihrke (through December, 2012)
    • Pauline Funk (through January, 2013)
  • Brandeis University
    • James Pustejovsky

Publications and presentations crediting THYME


  • Albright, Daniel; Lanfranchi, Arrick; Fredriksen, Anwen; Styler, William; Warner, Collin; Hwang, Jena; Choi, Jinho; Dligach, Dmitriy; Nielsen, Rodney; Martin, James; Ward, Wayne; Palmer, Martha; Savova, Guergana. 2013. Towards syntactic and semantic annotations of the clinical narrative. Journal of the American Medical Informatics Association. 2013;0:1–9. doi:10.1136/amiajnl-2012-001317; PMID: 23355458
  • Chen, Wei-Te and Styler, Will. 2013. Anafora: A Web-based General Purpose Annotation Tool. Proceeding of the North American Association for Computational Linguistics Conference. Atlanta, GA, June 9-13.
  • Miller, Timothy; Bethard, Steven; Dligach, Dmitriy; Pradhan, Sameer; Lin, Chen; and Savova, Guergana. 2013. Discovering narrative containers in clinical text. BioNLP workshop at the Association for Computational Linguistics.
  • Bethard, Steven. 2013. A Synchronous Context Free Grammar for Time Normalization. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing.
  • Bethard, Steven. 2013. ClearTK-TimeML: A minimalist approach to TempEval 2013. In: Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013). Atlanta, Georgia, USA: Association for Computational Linguistics, pp. 10-14.
  • Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Hwee Tou Ng, Anders Bjorkelund, Olga Uryupina, Yuchen Zhang and Zhi Zhong. 2013. Towards Robust Linguistic Analysis Using OntoNotes. Proceedings of the Conference on Natural Language Learning. Sofia, Bulgaria. August, 2013.
  • Dligach, Dmitriy; Bethard, Steven; Becker, Lee; Miller, Timothy; Savova, Guergana. 2013. Discovering body site and severity modifiers in clinical texts. Journal of the American Medical Informatics Association.
  • Finan, Sean. 2013. Challenges of visually representing rich temporal information of the clinical narrative. Workshop: Exploring Temporal Patterns in Electronic Health Record Data. 30th Annual Human-Computer Interaction Lab Symposium. May 22-23 2013. University of Maryland.
  • American Medical Informatics Association (AMIA) national webinar. “Towards semantic annotations of the clinical narrative”. National webinar. April 2013 (invited presentation)
  • Natural Language Processing Working Group Pre-Symposium – doctoral consortium and a data workshop. “Shared Annotated Resources for the Clinical Domain”. American Medical Informatics Association. Washington, DC, USA. November 2013.
  • Savova, Guergana; Chapman, Wendy; Elhadad, Noemie; Palmer, Martha. 2013. Shared resources, shared code and shared activities in clinical natural language processing. AMIA Annual Symposium, Panel. Washington, DC.
  • AMIA Fall symposium workshop on Natural Language Processing and data. Dr. Savova presented THYME work as part of the data workshop.


  • Savova, Guergana. 2012. Shared Annotated Resources for the Clinical Domain. Natural Language Processing (NLP) Annotation workshop collocated with the 2nd annual IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology. San Diego, CA, USA. September 2012.
  • Drs. Pustejovsky, Palmer and Savova are members of the Program Committee of the 2012 i2b2 shared task whose topic is temporal relations in the clinical domain. The THYME annotation guidelines are the basis of the annotation guidelines for that shared task.
  • Participation in the State of the Art of Clinical NLP workshop organized by the NLM in April, 2012. Dr. Savova chaired a session, Prof. Pustejovsky was an invited speaker presenting on Temporal relations/TimeML.
  • Participation and presentation in the AMIA Fall symposium workshop on Natural Language Processing and data. Dr. Savova presented THYME work as part of the data workshop.


  • Savova, Guergana; Chapman, Wendy; Elhadad, Noemie; Palmer, Martha. 2011. Shared annotated resources for the clinical domain. AMIA Annual Symposium, Panel. Washington, DC.

THYME Annotation Guidelines

These guidelines were provided to the organizers of the 2012 Temporal relations i2b2 challenge for consideration during planning, and reflect an earlier stage of our guidelines. As such, although representative, These guidelines are out of date. Please check back in March, 2014 for a more up-to-date copy of the guidelines.

Annotations and availability

Annotation layers are treebank and propbank annotations as well as temporal annotations for events, temporal expressions and temporal relations. The corpus will be made available to the research community under a data use agreement. Instructions as to how to get the corpus will be posted soon.

Viewing annotations (Anafora)

(available to the team only)

To view the Temporal-Entity data, use the URL:

TASK_NAME is the filestem, for example, ID074_path_219b

to view Temporal-Relation data:

you could find the available Entity/Relation gold data on verbs by using:

  find /data/anafora/anaforaProjectFile/Temporal/ -name "*"
  find /data/anafora/anaforaProjectFile/Temporal/ -name "*"

THYME system

THYME system is available as part of Apache cTAKES at Team members can track development progress.

Relevant Papers

Relevant Papers

Internal Presentations


Venues for manuscript submissions

Venues for manuscript submissions/publications

Project materials

Project Charter

Tasks, leads, teams and deadlines

Progress reports

Clinical Temporal Relations Annotation Guidelines - Release notes and latest versions

Annotations - Describes the corpus, the layers of annotations and annotation progress

Annotation Tools - Describes the progress and information pertaining to the Anafora annotation tool

Software - Describes the software modules and their organization

Train/Development/Test splits

  • Use this split for experiments with the THYME data (% 8)!

Colon Cancer Data

  • Train sets:(Residue 0,1,2,3) 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27, 32, 33, 34, 35, 40, 41, 43, 48, 49, 50, 51, 56, 58, 59, 64, 65, 66, 67, 72, 73, 74, 90, 114, 120
  • Development sets: (Residue 4,5) 4, 12, 13, 20, 21, 28, 29, 36, 44, 45, 52, 53, 60, 61, 68, 69, 76
  • Test sets: (Residue 6,7) 6, 7, 14, 15, 22, 23, 30, 31, 38, 39, 46, 47, 54, 55, 62, 63, 70, 71, 94

Brain Cancer Data

  • Train sets: 2, 3, 8, 9
  • Development sets: 4, 5
  • Test sets: 6, 7


Meeting Notes

Getting started


If you need assistance and/or if you have questions about the project, feel free to send e-mail to steven.bethard at colorado dot edu OR Guergana.Savova at childrens dot harvard dot edu

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