ArticleJournal of healthcare engineering2018
Unsupervised Medical Entity Recognition and Linking in Chinese Online Medical Text.
Article in Journal of healthcare engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Enhancing unsupervised medical entity linking with multi-instance learning.BMC medical informatics and decision making · 2021Article
- Cross-lingual Unified Medical Language System entity linking in online health communities.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- "Similar query was answered earlier": processing of patient authored text for retrieving relevant contents from health discussion forum.Health information science and systems · 2019Article
- Minimalistic Approach to Coreference Resolution in Lithuanian Medical Records.Computational and mathematical methods in medicine · 2019Article
- Clinical Named Entity Recognition From Chinese Electronic Health Records via Machine Learning Methods.JMIR medical informatics · 2018Article
- Unsupervised SapBERT-based bi-encoders for medical concept annotation of clinical narratives with SNOMED CT.Digital healthArticle
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Authors and funding
4 authors.
Funding
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Abstract
Online medical text is full of references to medical entities (MEs), which are valuable in many applications, including medical knowledge-based (KB) construction, decision support systems, and the treatment of diseases. However, the diverse and ambiguous nature of the surface forms gives rise to a great difficulty for ME identification. Many existing solutions have focused on supervised approaches, which are often task-dependent. In other words, applying them to different kinds of corpora or identifying new entity categories requires major effort in data annotation and feature definition. In this paper, we propose unMERL, an unsupervised framework for recognizing and linking medical entities mentioned in Chinese online medical text. For ME recognition, unMERL first exploits a knowledge-driven approach to extract candidate entities from free text. Then, the categories of the candidate entities are determined using a distributed semantic-based approach. For ME linking, we propose a collaborative inference approach which takes full advantage of heterogenous entity knowledge and unstructured information in KB. Experimental results on real corpora demonstrate significant benefits compared to recent approaches with respect to both ME recognition and linking.
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Registered trials
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