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外文期刊>JMIR Medical Informatics
>Temporal Expression Classification and Normalization From Chinese Narrative Clinical Texts: Pattern Learning Approach
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Temporal Expression Classification and Normalization From Chinese Narrative Clinical Texts: Pattern Learning Approach
Background Temporal information frequently exists in the representation of the disease progress, prescription, medication, surgery progress, or discharge summary in narrative clinical text. The accurate extraction and normalization of temporal expressions can positively boost the analysis and understanding of narrative clinical texts to promote clinical research and practice. Objective The goal of the study was to propose a novel approach for extracting and normalizing temporal expressions from Chinese narrative clinical text. Methods TNorm, a rule-based and pattern learning-based approach, has been developed for automatic temporal expression extraction and normalization from unstructured Chinese clinical text data. TNorm consists of three stages: extraction, classification, and normalization. It applies a set of heuristic rules and automatically generated patterns for temporal expression identification and extraction of clinical texts. Then, it collects the features of extracted temporal expressions for temporal type prediction and classification by using machine learning algorithms. Finally, the features are combined with the rule-based and a pattern learning-based approach to normalize the extracted temporal expressions. Results The evaluation dataset is a set of narrative clinical texts in Chinese containing 1459 discharge summaries of a domestic Grade A Class 3 hospital. The results show that TNorm, combined with temporal expressions extraction and temporal types prediction, achieves a precision of 0.8491, a recall of 0.8328, and a F1 score of 0.8409 in temporal expressions normalization. Conclusions This study illustrates an automatic approach, TNorm, that extracts and normalizes temporal expression from Chinese narrative clinical texts. TNorm was evaluated on the basis of discharge summary data, and results demonstrate its effectiveness on temporal expression normalization.
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