Data-Driven Information Extraction from Chinese Electronic Medical Records
OBJECTIVE: This study aims to propose a data-driven framework that takes unstructured free text narratives in Chinese Electronic Medical Records (EMRs) as input and converts them into structured time-event-description triples, where the description is either an elaboration or an outcome of the medical event.
MATERIALS AND METHODS: Our framework uses a hybrid approach. It consists of constructing cross-domain core medical lexica, an unsupervised, iterative algorithm to accrue more accurate terms into the lexica, rules to address Chinese writing conventions and temporal descriptors, and a Support Vector Machine (SVM) algorithm that innovatively utilizes Normalized Google Distance (NGD) to estimate the correlation between medical events and their descriptions.
RESULTS: The effectiveness of the framework was demonstrated with a dataset of 24,817 de-identified Chinese EMRs. The cross-domain medical lexica were capable of recognizing terms with an F1-score of 0.896. 98.5% of recorded medical events were linked to temporal descriptors. The NGD SVM description-event matching achieved an F1-score of 0.874. The end-to-end time-event-description extraction of our framework achieved an F1-score of 0.846.
DISCUSSION: In terms of named entity recognition, the proposed framework outperforms state-of-the-art supervised learning algorithms (F1-score: 0.896 vs. 0.886). In event-description association, the NGD SVM is superior to SVM using only local context and semantic features (F1-score: 0.874 vs. 0.838).
CONCLUSIONS: The framework is data-driven, weakly supervised, and robust against the variations and noises that tend to occur in a large corpus. It addresses Chinese medical writing conventions and variations in writing styles through patterns used for discovering new terms and rules for updating the lexica.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2015 |
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| Erschienen: |
2015 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
PloS one - 10(2015), 8 vom: 16., Seite e0136270 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Xu, Dong [VerfasserIn] |
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| Links: |
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| Themen: |
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| Anmerkungen: |
Date Completed 13.05.2016 Date Revised 13.11.2018 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.1371/journal.pone.0136270 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM25204763X |
|---|
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| 500 | |a Date Revised 13.11.2018 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a OBJECTIVE: This study aims to propose a data-driven framework that takes unstructured free text narratives in Chinese Electronic Medical Records (EMRs) as input and converts them into structured time-event-description triples, where the description is either an elaboration or an outcome of the medical event | ||
| 520 | |a MATERIALS AND METHODS: Our framework uses a hybrid approach. It consists of constructing cross-domain core medical lexica, an unsupervised, iterative algorithm to accrue more accurate terms into the lexica, rules to address Chinese writing conventions and temporal descriptors, and a Support Vector Machine (SVM) algorithm that innovatively utilizes Normalized Google Distance (NGD) to estimate the correlation between medical events and their descriptions | ||
| 520 | |a RESULTS: The effectiveness of the framework was demonstrated with a dataset of 24,817 de-identified Chinese EMRs. The cross-domain medical lexica were capable of recognizing terms with an F1-score of 0.896. 98.5% of recorded medical events were linked to temporal descriptors. The NGD SVM description-event matching achieved an F1-score of 0.874. The end-to-end time-event-description extraction of our framework achieved an F1-score of 0.846 | ||
| 520 | |a DISCUSSION: In terms of named entity recognition, the proposed framework outperforms state-of-the-art supervised learning algorithms (F1-score: 0.896 vs. 0.886). In event-description association, the NGD SVM is superior to SVM using only local context and semantic features (F1-score: 0.874 vs. 0.838) | ||
| 520 | |a CONCLUSIONS: The framework is data-driven, weakly supervised, and robust against the variations and noises that tend to occur in a large corpus. It addresses Chinese medical writing conventions and variations in writing styles through patterns used for discovering new terms and rules for updating the lexica | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Zhang, Meizhuo |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Tianwan |e verfasserin |4 aut | |
| 700 | 1 | |a Ge, Chen |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Weiguo |e verfasserin |4 aut | |
| 700 | 1 | |a Wei, Jia |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Kenny Q |e verfasserin |4 aut | |
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