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Updated: May 21, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

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

Siddhartha Reddy Jonnalagadda¹, Dingcheng Li, Sunghwan Sohn

¹Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55905, USA. siddhartha@mayo.edu

Journal of the American Medical Informatics Association : JAMIA

|June 19, 2012

Summary

Mayo Clinic developed MedCoref, a multi-pass sieve coreference resolution system for clinical notes. This system effectively links entities in medical documents, achieving high performance in a shared task.

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Last Updated: May 21, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Area of Science:

Natural Language Processing
Clinical Informatics
Information Extraction

Background:

Coreference resolution is crucial for understanding clinical narratives.
Accurate entity linking in medical records aids downstream applications.
Previous systems struggled with data irregularities and limited examples.

Purpose of the Study:

To describe the MedCoref system for the 2011 i2b2/VA/Cincinnati shared task.
To develop a system capable of linking markables referring to the same entity.
To improve coreference resolution in clinical progress notes and discharge summaries.

Main Methods:

Utilized a multi-pass sieve algorithm with deterministic rules applied sequentially.
Incorporated a machine learning framework as an alternative to rule-based pronoun resolution.
Annotated clinical data included markables for treatment, problem, test, person, and pronoun.

Main Results:

The multi-pass sieve system achieved an F-score of 0.836 on the training set.
The system obtained an F-score of 0.843 on the test set.
Demonstrated effectiveness using simple rules, part-of-speech, and semantic type properties.

Conclusions:

A sieve-based framework effectively combines machine learning with expert-designed rules.
This approach accommodates data irregularities better than single-function supervised systems.
The MedCoref system offers an effective solution for clinical coreference resolution.