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Related Experiment Video

Updated: Mar 30, 2026

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Published on: August 12, 2019

Multilayered temporal modeling for the clinical domain.

Chen Lin¹, Dmitriy Dligach², Timothy A Miller²

¹Boston Children's Hospital Boston, Boston, Massachusetts, USA chen.lin@childrens.harvard.edu.

Journal of the American Medical Informatics Association : JAMIA

|November 2, 2015

Summary

This summary is machine-generated.

We developed an open-source system for discovering temporal relations in clinical text. This system achieves state-of-the-art performance, setting new benchmarks for clinical temporal relation discovery.

Keywords:

Allen's temporal interval relations document creation time electronic medical record narrative container natural language processing temporal relation discovery

Related Experiment Videos

Last Updated: Mar 30, 2026

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Published on: August 12, 2019

Area of Science:

Natural Language Processing
Clinical Informatics
Machine Learning

Background:

Temporal relation discovery is crucial for understanding clinical narratives.
Existing systems often lack the flexibility to handle diverse temporal expressions in clinical data.

Purpose of the Study:

To develop an open-source system for automatic temporal relation discovery in the clinical domain.
To infer temporal relations between events and time expressions at various granularities.

Main Methods:

A multilayered modeling strategy was employed for temporal relation inference.
Supervised machine learning models were developed for document creation time (DCT) and within-sentence relations.
Rule-based methods were used for cross-sentence temporal relations on the i2b2 corpus.

Main Results:

The system achieved state-of-the-art performance on the Clinical TempEval corpus, establishing a new F1 score benchmark.
Performance was on par with the best systems on the 2012 i2b2 challenge corpus.
A feature ablation study identified key contributors to system performance.

Conclusions:

The first open-source clinical temporal relation discovery system has been presented.
The system utilizes a multilayered temporal modeling strategy.
Top performance was achieved in two major clinical NLP shared tasks.