Evidence map›Paper›PMID 22707745›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA

Coreference analysis in clinical notes: a multi-pass sieve with alternate anaphora resolution modules.

Siddhartha Reddy Jonnalagadda, Dingcheng Li, Sunghwan Sohn, Stephen Tze-Inn Wu, Kavishwar Wagholikar, Manabu Torii, Hongfang Liu

Abstract readEvaluation StudyMulticenter Study
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. PIPE: a protein-protein interaction passage extraction module for BioCreative challenge.Database : the journal of biological databases and curation · 2016
    Article
  6. Article
  7. An Infinite Mixture Model for Coreference Resolution in Clinical Notes.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2016
    Article
  8. Article
  9. Article
  10. Article
  11. "Big data" and the electronic health record.Yearbook of medical informatics · 2014
    Review
  12. Electronic health records-driven phenotyping: challenges, recent advances, and perspectives.Journal of the American Medical Informatics Association : JAMIA · 2013
    Article
  13. Article
  14. Article
  15. Patient-level temporal aggregation for text-based asthma status ascertainment.Journal of the American Medical Informatics Association : JAMIA
    Article
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

7 authors.

Siddhartha Reddy JonnalagaddaDepartment of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55905, USA. siddhartha@mayo.edu
Dingcheng Li
Sunghwan Sohn
Stephen Tze-Inn Wu
Kavishwar Wagholikar
Manabu Torii
Hongfang Liu

Funding

Informatics for Integrating Biology/ the Bedside (RMI)U54LM008748 · NLM · BRIGHAM AND WOMEN'S HOSPITAL · PI KOHANE, ISAAC S. · 2004 to 2013
$39.0M
Natural language processing for clinical and translational researchR01GM102282 · NIGMS · MAYO CLINIC ROCHESTER · PI LIU, HONGFANG, PAKHOMOV, SERGUEI VS · 2013 to 2016
$2.5M
Onto-BioThesaurus: ontological representation of gene/protein names for biomedicaR01LM009959 · NLM · MAYO CLINIC ROCHESTER · PI LIU, HONGFANG · 2009 to 2010
$1.2M
Challenges in Natural Language Processing in Clinical TextR13LM011411 · NLM · STATE UNIVERSITY OF NEW YORK AT ALBANY · PI UZUNER, OZLEM · 2012 to 2016
$98k
NIGMS NIH HHS R01 GM102282NLM NIH HHS R01 LM009959NLM NIH HHS R01LM009959A1NLM NIH HHS R13 LM011411NLM NIH HHS U54 LM008748
6 · The paper itself

Abstract

objectiveThis paper describes the coreference resolution system submitted by Mayo Clinic for the 2011 i2b2/VA/Cincinnati shared task Track 1C. The goal of the task was to construct a system that links the markables corresponding to the same entity. MATERIALS AND

methodsThe task organizers provided progress notes and discharge summaries that were annotated with the markables of treatment, problem, test, person, and pronoun. We used a multi-pass sieve algorithm that applies deterministic rules in the order of preciseness and simultaneously gathers information about the entities in the documents. Our system, MedCoref, also uses a state-of-the-art machine learning framework as an alternative to the final, rule-based pronoun resolution sieve.

resultsThe best system that uses a multi-pass sieve has an overall score of 0.836 (average of B(3), MUC, Blanc, and CEAF F score) for the training set and 0.843 for the test set. DISCUSSION: A supervised machine learning system that typically uses a single function to find coreferents cannot accommodate irregularities encountered in data especially given the insufficient number of examples. On the other hand, a completely deterministic system could lead to a decrease in recall (sensitivity) when the rules are not exhaustive. The sieve-based framework allows one to combine reliable machine learning components with rules designed by experts.

conclusionUsing relatively simple rules, part-of-speech information, and semantic type properties, an effective coreference resolution system could be designed. The source code of the system described is available at https://sourceforge.net/projects/ohnlp/files/MedCoref.

Indexed as

Artificial IntelligenceDecision Support Systems, ClinicalElectronic Health RecordsNatural Language ProcessingAlgorithmsData MiningHumansMarkov ChainsSemanticsSensitivity and SpecificityUnited States

Identifiers

PMID22707745
PMCPMC3422831

What OpenQuestion holds

Textmetadata
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.