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

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Enhanced ICD-10 code assignment of clinical texts: A summarization-based approach.

Yaoqian Sun¹, Lei Sang², Dan Wu¹

¹College of Biomedical Engineering and Instrument Science, Zhejiang University, Zheda Road, 310027 Hangzhou, Zhejiang Province, China.

Artificial Intelligence in Medicine

|August 29, 2024

Summary

This study introduces a novel summarization-based approach for International Classification of Diseases (ICD) coding, improving accuracy in assigning ICD-10 codes to clinical texts. The method effectively distills key information, outperforming traditional classification techniques.

Keywords:

Automated ICD-10 code assignment Natural Language Processing Text matching Text summarization

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Published on: February 23, 2019

Area of Science:

Medical Informatics
Natural Language Processing
Health Data Science

Background:

Assigning International Classification of Diseases (ICD) codes is vital for patient classification, hospital management, and statistical analysis.
Current automated ICD coding methods often struggle with high-dimensional spaces and redundant information in lengthy clinical texts.
Existing multi-label classification approaches face challenges with long documents and vast coding spaces.

Purpose of the Study:

To develop an efficient and accurate automated approach for assigning International Classification of Diseases, Tenth Revision (ICD-10) codes to clinical texts.
To address the limitations of existing multi-label classification methods in handling long clinical texts and large label spaces.
To introduce text summarization techniques into the ICD coding process for improved information distillation.

Main Methods:

A novel summarization-based approach (SuM) using a knowledge-guided pointer network to distill and summarize clinical text information.
An end-to-end strategy for efficient ICD-10 code assignment.
A matching model with a matching-aggregation architecture to align summaries with ICD codes, shifting from one-vs-all to one-vs-one matching.

Main Results:

The SuM approach achieved superior performance on a dataset of 12,788 ICD-10 coded discharge summaries.
Achieved a Micro AUC of 0.9548 and MRR@10 of 0.7977 on the TOP-50 Dataset.
Demonstrated consistent results on the FULL-Dataset, indicating the efficacy of the knowledge encoder and end-to-end strategy.

Conclusions:

The proposed automatic ICD-10 code assignment approach effectively captures critical information from long clinical texts.
Text summarization integrated with matching models significantly enhances the performance of clinical text ICD-10 coding.
The SuM approach offers a promising solution for improving the accuracy and efficiency of automated ICD coding.