# Temporal relation discovery for clinical text

> **NIH NIH R01** · BOSTON CHILDREN'S HOSPITAL · 2021 · $531,398

## Abstract

Project Summary / Abstract
The current proposal continues the investigation on the topic of temporal relation extraction from the Electronic
Medical Records (EMR) clinical narrative funded by the NLM since 2010 (Temporal Histories of Your Medical
Events, or THYME; thyme.healthnlp.org). Through our efforts so far, we have defined the topic as an active
area of research attracting attention across the world. Since its inception, the project has pushed the
boundaries of this highly challenging task by investigating new computational methods within the context of the
latest developments in the fields of natural language processing (NLP), machine learning (ML), artificial
intelligence (AI) and biomedical informatics (BMI) resulting in 60+ publications/presentations. We have made
our best performing methods available to the community open source as part of the Apache Clinical Text
Analysis and Knowledge Extraction System (cTAKES; ctakes.apache.org). In 2015, 2016, 2017 and 2018, we
organized an international shared task (Clinical TempEval) on the topic under the umbrella of the highly
prestigious SemEval, thus inviting the international community to work with our THYME data and improve on
our results. Clinical TempEval has been highly successful with many participants each year, resulting in new
discoveries and many publications. We have made all our data along with our gold standard annotations
available to the community through the hNLP Center (center.healthnlp.org).
 The underlying theme of this renewal is novel methods for combining explicit domain knowledge (linguistic,
semantic, biomedical ontological, clinical), readily available unlabeled data (health-related social media,
EMRs), and modern machine learning techniques (e.g. neural networks) for temporal relation extraction from
the EMR clinical narrative. Therefore, our renewal proposes a novel and much needed exploration of this line
of research:
 Specific Aim 1: Develop computational models for novel rich semantic representations such as the
Abstract Meaning Representations to encapsulate a single, coherent, full-document graphical representation of
meaning for temporal relation extraction
 Specific Aim 2: Develop computational methods to infuse domain knowledge (linguistic, semantic,
biomedical ontological, clinical) into modern machine learning techniques such as NNs for temporal relation
extraction – through input representations, pre-trained vectors, or architectures
 Specific Aim 3: Develop novel methods for combining labeled and unlabeled data from various sources
(EMR, health-related social media, newswire) for temporal relation extraction from the clinical narrative
 Specific Aim 4: Apply the best performing methods for temporal relation extraction developed in SA1-3 to
temporally sensitive phenotypes for direct translational sciences studies. Dissemination efforts through
publications and open source releases into Apache cTAKES.

## Key facts

- **NIH application ID:** 10176589
- **Project number:** 5R01LM010090-10
- **Recipient organization:** BOSTON CHILDREN'S HOSPITAL
- **Principal Investigator:** Martha Stone Palmer
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $531,398
- **Award type:** 5
- **Project period:** 2010-09-30 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10176589

## Citation

> US National Institutes of Health, RePORTER application 10176589, Temporal relation discovery for clinical text (5R01LM010090-10). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10176589. Licensed CC0.

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