ArticleBMC medical informatics and decision making2018
A pattern learning-based method for temporal expression extraction and normalization from multi-lingual heterogeneous clinical texts.
Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Improving Retrieval Augmented Generation for Health Care by Fine-Tuning Clinical Embedding Models: Development and Evaluation Study.Journal of medical Internet research · 2026Article
- Developing a Natural Language Processing tool to identify perinatal self-harm in electronic healthcare records.PloS one · 2021Article
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- Temporal Expression Classification and Normalization From Chinese Narrative Clinical Texts: Pattern Learning Approach.JMIR medical informatics · 2020Article
- Precursor-induced conditional random fields: connecting separate entities by induction for improved clinical named entity recognition.BMC medical informatics and decision making · 2019Article
- Correction to: A pattern learning-based method for temporal expression extraction and normalization from multi-lingual heterogeneous clinical texts.BMC medical informatics and decision making · 2018Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTemporal expression extraction and normalization is a fundamental and essential step in clinical text processing and analyzing. Though a variety of commonly used NLP tools are available for medical temporal information extraction, few work is satisfactory for multi-lingual heterogeneous clinical texts.
methodsA novel method called TEER is proposed for both multi-lingual temporal expression extraction and normalization from various types of narrative clinical texts including clinical data requests, clinical notes, and clinical trial summaries. TEER is characterized as temporal feature summarization, heuristic rule generation, and automatic pattern learning. By representing a temporal expression as a triple <M, A, N>, TEER identifies temporal mentions M, assigns type attributes A to M, and normalizes the values of M into formal representations N.
resultsBased on two heterogeneous clinical text datasets: 400 actual clinical requests in English and 1459 clinical discharge summaries in Chinese. TEER was compared with six state-of-the-art baselines. The results showed that TEER achieved a precision of 0.948 and a recall of 0.877 on the English clinical requests, while a precision of 0.941 and a recall of 0.932 on the Chinese discharge summaries.
conclusionsAn automated method TEER for multi-lingual temporal expression extraction was presented. Based on the two datasets containing heterogeneous clinical texts, the comparison results demonstrated the effectiveness of the TEER method in multi-lingual temporal expression extraction from heterogeneous narrative clinical texts.
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