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Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

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Temporal Expression Classification and Normalization From Chinese Narrative Clinical Texts: Pattern Learning

Xiaoyi Pan¹, Boyu Chen¹, Heng Weng²

¹School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China.

JMIR Medical Informatics

|July 28, 2020

Summary

This summary is machine-generated.

This study introduces TNorm, a novel method for extracting and normalizing temporal expressions in Chinese clinical texts. TNorm effectively processes discharge summaries, improving clinical data analysis.

Keywords:

Clinical text Heuristic rule Machine learning Pattern learning Temporal expression extraction Temporal expression normalization

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Area of Science:

Natural Language Processing
Clinical Informatics
Artificial Intelligence

Background:

Clinical narratives contain vital temporal information for disease progression and treatment tracking.
Accurate extraction and normalization of temporal expressions enhance clinical research and practice.

Purpose of the Study:

To develop and evaluate TNorm, a new approach for automatic temporal expression extraction and normalization in Chinese clinical text.

Main Methods:

TNorm employs a three-stage process: extraction, classification, and normalization.
It utilizes heuristic rules, pattern learning, and machine learning for identifying and standardizing temporal expressions.
The system was tested on 1459 Chinese discharge summaries.

Main Results:

TNorm achieved a precision of 0.8491, recall of 0.8328, and F1 score of 0.8409 for temporal expression normalization.
The combined approach of extraction, prediction, and normalization proved effective.

Conclusions:

TNorm offers an effective automatic solution for temporal expression extraction and normalization in Chinese clinical text.
Evaluation on discharge summaries confirms TNorm's capability in enhancing clinical data analysis.