Evidence map›Paper›PMID 24212118›Full record

ArticleJournal of biomedical informatics2013

Towards generating a patient's timeline: extracting temporal relationships from clinical notes.

Azadeh Nikfarjam, Ehsan Emadzadeh, Graciela Gonzalez

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of biomedical informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Applications of network analysis to routinely collected health care data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2018
    Pooled it
  2. Article
  3. Review
  4. Article
  5. Typed Markers and Context for Clinical Temporal Relation Extraction.Proceedings of machine learning research · 2023
    Article
  6. Article
  7. Review
  8. Review
  9. Review
  10. Desiderata for computable representations of electronic health records-driven phenotype algorithms.Journal of the American Medical Informatics Association : JAMIA · 2015
    Article
  11. Scaling drug indication curation through crowdsourcing.Database : the journal of biological databases and curation · 2015
    Article
  12. Article
  13. Evaluating temporal relations in clinical text: 2012 i2b2 Challenge.Journal of the American Medical Informatics Association : JAMIA
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Azadeh NikfarjamDepartment of Biomedical Informatics, Arizona State University, Tempe, USA. Electronic address: anikfarj@asu.edu.
Ehsan EmadzadehDepartment of Biomedical Informatics, Arizona State University, Tempe, USA.
Graciela GonzalezDepartment of Biomedical Informatics, Arizona State University, Tempe, USA.

Funding

Informatics for Integrating Biology/ the Bedside (RMI)U54LM008748 · NLM · BRIGHAM AND WOMEN'S HOSPITAL · PI KOHANE, ISAAC S. · 2004 to 2013
$39.0M
Social Media Mining for PharmacovigilanceR01LM011176 · NLM · UNIVERSITY OF PENNSYLVANIA · PI GONZALEZ HERNANDEZ, GRACIELA, SHEN, LI · 2012 to 2021
$4.5M
Challenges in Natural Language Processing in Clinical TextR13LM011411 · NLM · STATE UNIVERSITY OF NEW YORK AT ALBANY · PI UZUNER, OZLEM · 2012 to 2016
$98k
NLM NIH HHS 1R01LM011176NLM NIH HHS 1R13LM011411-01NLM NIH HHS 2U54LM008748NLM NIH HHS R01 LM011176NLM NIH HHS R13 LM011411NLM NIH HHS U54 LM008748
6 · The paper itself

Abstract

Clinical records include both coded and free-text fields that interact to reflect complicated patient stories. The information often covers not only the present medical condition and events experienced by the patient, but also refers to relevant events in the past (such as signs, symptoms, tests or treatments). In order to automatically construct a timeline of these events, we first need to extract the temporal relations between pairs of events or time expressions presented in the clinical notes. We designed separate extraction components for different types of temporal relations, utilizing a novel hybrid system that combines machine learning with a graph-based inference mechanism to extract the temporal links. The temporal graph is a directed graph based on parse tree dependencies of the simplified sentences and frequent pattern clues. We generalized the sentences in order to discover patterns that, given the complexities of natural language, might not be directly discoverable in the original sentences. The proposed hybrid system performance reached an F-measure of 0.63, with precision at 0.76 and recall at 0.54 on the 2012 i2b2 Natural Language Processing corpus for the temporal relation (TLink) extraction task, achieving the highest precision and third highest f-measure among participating teams in the TLink track.

Indexed as

Electronic Health RecordsNatural Language ProcessingData MiningHumansMedical InformaticsSupport Vector MachineTime FactorsAutomatic patient timelineClinical text miningMachine learningNatural Language ProcessingTemporal graphTemporal relation extraction

Identifiers

PMID24212118
PMCPMC3974721

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.