JMIR Publications

ABSTRACT

Background:

Temporal information frequently exists in the representation of the disease progress, prescription, medication, the 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 so as to promote the clinical research and practice.

Objective:

The study is 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 expressions 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 1,459 discharge summaries of a domestic Grade-A Class-three hospital. The results present 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 summaries and demonstrated its effectiveness on temporal expression normalization with experiment results.