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Related Experiment Video

Updated: Jun 2, 2026

Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

MITRE system for clinical assertion status classification.

Cheryl Clark¹, John Aberdeen, Matt Coarr

¹The MITRE Corporation, Bedford, Massachusetts 01730-1420, USA. cclark@mitre.org

Journal of the American Medical Informatics Association : JAMIA

|April 26, 2011

Summary

This summary is machine-generated.

This study presents a system for classifying medical problem assertions in clinical reports using machine learning and rule-based methods. The system achieved a high F-score, demonstrating effective assertion status determination.

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Last Updated: Jun 2, 2026

Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Area of Science:

Natural Language Processing (NLP)
Clinical Informatics
Machine Learning

Background:

Accurate assertion status determination of medical problems in clinical reports is crucial for clinical decision support.
Existing methods for assertion classification often struggle with complex linguistic nuances in clinical text.

Purpose of the Study:

To describe a system for classifying the assertion status of medical problems in clinical reports.
To evaluate the system's performance in the 2010 i2b2/VA 'Challenges in natural language processing for clinical data' task.

Main Methods:

A hybrid approach combining machine learning (Conditional Random Field, Maximum Entropy) and rule-based techniques (pattern matching).
Features included semantic attributes of concepts and document structure for classification.
Detection of negation, speculation, hypothetical/conditional information, and patient-associated information.

Main Results:

The best performing system submission achieved a micro-averaged F-score of 0.9343.
Demonstrated the effectiveness of the combined machine learning and rule-based approach.

Conclusions:

Leveraging semantic attributes and document structure as features is effective for assertion classification.
Feature selection may be more critical than the choice of classifier algorithm for this task.