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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Extracting clinical terms from radiology reports with deep learning.

Kento Sugimoto¹, Toshihiro Takeda², Jong-Hoon Oh³

¹Department of Medical Informatics, Osaka University Graduate School of Medicine, Suita, Osaka, Japan; National Institute of Information and Communications Technology, Seika, Kyoto, Japan.

Journal of Biomedical Informatics

|March 12, 2021

Summary

This summary is machine-generated.

This study introduces a new information model for extracting clinical terms from radiology reports. The model effectively identifies observations, findings, and modifiers, demonstrating high accuracy and generalizability across datasets.

Keywords:

Deep Learning Information Extraction Natural Language Processing Radiology Report

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Area of Science:

Medical Informatics
Natural Language Processing
Radiology

Background:

Secondary use of clinical data from radiology reports is crucial.
Lack of consensus exists on extracting specific clinical terms.
Standardizing clinical term extraction is needed for data analysis.

Purpose of the Study:

To propose an information model for clinical term extraction from radiology reports.
To evaluate the performance of deep learning models for this task.
To assess the generalizability of the proposed model.

Main Methods:

Developed an information model with three clinical entity types: observations, clinical findings, and modifiers.
Utilized state-of-the-art deep learning models for term extraction.
Trained and evaluated models on 540 in-house chest computed tomography (CT) reports and external datasets.

Main Results:

Achieved a micro F1-score of 95.36% on in-house datasets and 94.62% on external datasets.
Demonstrated the suitability of the proposed information model for clinical term extraction.
Confirmed the model's generalizability to reports from different institutions.

Conclusions:

The proposed information model effectively extracts clinical terms from radiology reports.
Deep learning models, when trained with this model, show high accuracy and generalizability.
This approach facilitates the secondary use of radiology report data.