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Updated: Jun 4, 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

Clinical Case-based Retrieval Using Latent Topic Analysis.

Corey W Arnold¹, Suzie M El-Saden, Alex A T Bui

¹University of California, Medical Imaging Informatics Group, Los Angeles, CA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|February 25, 2011

Summary

This summary is machine-generated.

This study introduces a novel method for comparing patient clinical records using latent topics derived from report types. This approach enables quantitative patient similarity judgments and facilitates efficient case-based retrieval.

Related Experiment Videos

Last Updated: Jun 4, 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:

Computational linguistics
Medical informatics
Data science

Background:

Clinical records lack quantitative comparison methods.
Existing formats hinder computational analysis and patient similarity judgments.

Purpose of the Study:

To develop a method for quantitative comparison of patient clinical records.
To enable granular topic characterization by partitioning reports by type.
To facilitate case-based retrieval of similar patient records.

Main Methods:

Utilized latent topic modeling on partitioned clinical reports.
Generated probabilistic patient topic representations.
Developed a workstation for interactive navigation and similarity assessment.

Main Results:

The system successfully captured clinically significant topics.
Patient records were represented in a comparable quantitative space.
The workstation enabled tailored and expedited patient searches.

Conclusions:

The proposed method allows for effective quantitative comparison of patient records.
This approach enhances the utility of clinical data for computational analysis and case-based retrieval.