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A pattern learning-based method for temporal expression extraction and normalization from multi-lingual heterogeneous
Tianyong Hao1,2, Xiaoyi Pan1, Zhiying Gu1
1School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China.
BMC Medical Informatics and Decision Making
|March 29, 2018
Summary
A new method, TEER, effectively extracts and normalizes temporal expressions from multilingual clinical texts. This approach improves analysis of diverse medical documents, enhancing clinical NLP applications.
Area of Science:
- Natural Language Processing (NLP)
- Clinical Informatics
- Computational Linguistics
Background:
- Temporal expression extraction and normalization are crucial for clinical text analysis.
- Existing NLP tools often struggle with multilingual and heterogeneous clinical data.
Purpose of the Study:
- To introduce TEER, a novel method for multilingual temporal expression extraction and normalization.
- To address limitations of current NLP tools in handling diverse clinical text types.
Main Methods:
- TEER utilizes temporal feature summarization, heuristic rule generation, and automatic pattern learning.
- Temporal expressions are represented as
- The method identifies mentions, assigns attributes, and normalizes values.
Main Results:
- TEER achieved high precision (0.948) and recall (0.877) on English clinical requests.
- On Chinese discharge summaries, TEER demonstrated strong performance with precision (0.941) and recall (0.932).
- Comparative analysis showed TEER outperformed six state-of-the-art baselines.
Conclusions:
- TEER is an effective automated method for multilingual temporal expression extraction.
- The method shows significant promise for processing heterogeneous narrative clinical texts.
- TEER enhances the capabilities of clinical NLP in multilingual environments.
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