Multilayered temporal modeling for the clinical domain

February 11, 2016
Free for AMIA members and students of Academic Forum member institutions; Others: $50
Chen Lin, MA - Biomedical Informatician, Children's Hosp. Informatics Prgm, Children's Hospital Boston; Timothy Miller, PhD - Instructor, Harvard Medical School; Guergana Savova, PhD - Associate Professor, Boston Children's Hospital

Chen Lin, MA, Timothy Miller, PhD, and Guergana Savova, PhD, will discuss this month's JAMIA Journal Club selection:

Multilayered temporal modeling for the clinical domain

Complete Citation:
Lin C, Dligach D, Miller TA, Bethard S, Savova GK. 
Multilayered temporal modeling for the clinical domain. J Am Med Inform Assoc. 2015 Oct 31. pii: ocv113. doi: 10.1093/jamia/ocv113. [Epub ahead of print]


Chen Lin, MA is a biomedical informatician in the Children’s Hospital Informatics Program-Natural Language Processing (CHIP-NLP) Lab at Boston Children’s Hospital. 

Chen researches, develops, and implements statistical machine learning methods and Natural Language Processing (NLP) techniques to solve cutting-edge NLP and biomedical informatics problems. Topics include drug-induced adverse events identification, clinical temporal relation discovery, disease severity classification based on clinical narratives, etc. 

Timothy Miller, PhD is an Instructor at Harvard Medical School. He has worked in the field of biomedical and clinical informatics on a variety of NLP problems, including clinical coreference resolution, UMLS and temporal relation extraction, concept attribute classification, sentence segmentation, temporal expression extraction, and grammar induction. His research focuses on machine learning approaches to these problems, and with particular recent interest in semi-supervised and unsupervised approaches.

Guergana Savova, PhD is Associate Professor at Harvard Medical School. Her research focus is higher level semantic and discourse processing of the clinical narrative, which includes tasks such as named entity recognition, event recognition, relation detection and classification including coreference and temporal relations. The result of Dr. Savova's research with her collaborators has led to the creation of the clinical Text Analysis and Knowledge Extraction System (cTAKES; - a top-level Apache Software Foundation project.  cTAKES is an information extraction system comprising of a number of NLP components. cTAKES has been applied to a number of biomedical use cases to mine the data within the clinical narrative such as i2b2, PGRN, and eMERGE to name a few.  


  • 40-minute discussion between the authors and the JAMIA Student Editorial Board moderators including salient features of the published study and its potential impact on practice.
  • 20-minute discussion of questions submitted by listeners via the webinar tools.


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JAMIA Journal Club managers are JAMIA Student Editorial Board members:

Mary Regina Boland, MA, Department of Biomedical Informatics, Columbia University

Matthew K. Breitenstein, PhD, Department of Health Sciences Research, Mayo Clinic


The PubMed citation for the paper under discussion is:

Lin C, Dligach D, Miller TA, et al. 
Multilayered temporal modeling for the clinical domain. J Am Med Inform Assoc. 2015 Oct 31. pii: ocv113. doi: 10.1093/jamia/ocv113. [Epub ahead of print]

The abstract is available here: 

Fee Statement

Students who are not AMIA members, but whose academic institutions are members of the Academic Forum, are eligible for a complimentary JAMIA Journal Club registration. Please contact Susanne Arnold at for the discount code. In the email, please include: full name, Academic Department, and the primary Academic Forum representative of that Academic Department. Note that AMIA Student memberships are $45, which allow access to JAMIA, all JAMIA Journal Clubs, and other webinars of interest to the biomedical informatics community. 

Statement of Purpose

Temporality is crucial for a deeper understanding of the course of clinical events in a patient’s electronic medical records. A large part of it is recorded in the electronic medical record’s free text. Automatic temporal relation discovery has the potential to dramatically increase the understanding of many medical phenomena such as disease progression, longitudinal effects of medications, and a patient’s clinical course.

The Lin, et al study used a multilayered temporal modeling strategy to take advantage of automatic inference, reduce the complexity of temporal reasoning, and improve accuracy, especially at the macro level.

Scientists with interest in natural language processing will want to consider the clinical applications of the extraction and interpretation of temporal relations discussed in this study, including question answering, clinical outcomes prediction, and the recognition of temporal patterns and timelines.

Target Audience

The target audience for this activity is professionals and students interested in biomedical and health informatics.

Learning Objective

After this live activity, the participant should be better able to:

  • Weigh the clinical implications of a temporal relation discovery system applied to the clinical narrative within the electronic medical record.


Chen Lin, MS
Biomedical Informatician
Children's Hospital Informatics Program - Natural Language Processing Lab
Boston Children's Hospital
Boston, MA

Timothy Miller, PhD
Boston Children's Hospital
Boston, MA

Guergana Savova, PhD
Associate Professor, Boston Children's Hospital and Harvard Medical School
Principal Investigator, Clinical Natural Language Processing Program
Boston Children's Hospital
Boston, MA

Accreditation Statement

The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

Credit Designation Statement

The American Medical Informatics Association designates this live activity for a maximum of 1 AMA PRA Category 1 Credit(s). Physicians should claim only the credit commensurate with the extent of their participation in the activity. 

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Completion of this live activity is demonstrated by:

  • Viewing the live webinar
  • Optional submission of questions via webinar feature; option to follow @AMIAinformatics and tweet via #JAMIAJC
  • Completion of the evaluation survey at and 
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The physician participant will be able to generate a CME certificate through the AMIA automated system. 
For a certificate of completion, contact

Commercial Support

No commercial support was received for this activity.

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The ACCME considers relationships of the person involved in the CME activity to include financial relationships of a spouse or partner.

Faculty and planners who refuse to disclose relevant financial relationships will be disqualified from participating in the CME activity. For an individual with no relevant financial relationship(s), the participants must be informed that no conflicts of interest or financial relationship(s) exist.

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Disclosures for this Activity

These faculty, planners, and staff who are in a position to control the content of this activity disclose that they and their life partners have no relevant financial relationships with commercial interests: 

Faculty: Chen Lin, MA
JAMIA Journal Club planners: Mary Regina Boland, Matthew Breitenstein
AMIA staff: Susanne Arnold, Pesha Rubinstein

Faculty Timothy Miller discloses that he is a retained consultant for Wired Informatics, LLC.

Faculty Guergana Savova discloses that she is on the advisory board of Wired Informatics, LLC. 

JAMIA Journal Club planner Michael Chiang discloses the following:

  • Received Grant/Research support from the National Institutes of Health
  • Is an unpaid member of the Scientific Advisory Board of Clarity Medical Systems

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Contact Info

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