ArticleBMC medical informatics and decision making2019
Precursor-induced conditional random fields: connecting separate entities by induction for improved clinical named entity recognition.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Information extraction from green channel textual records on expressways using hybrid deep learning.Scientific reports · 2024Article
- Extracting clinical named entity for pituitary adenomas from Chinese electronic medical records.BMC medical informatics and decision making · 2022Article
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Authors and funding
2 authors.
Funding
Abstract
backgroundThis paper presents a conditional random fields (CRF) method that enables the capture of specific high-order label transition factors to improve clinical named entity recognition performance. Consecutive clinical entities in a sentence are usually separated from each other, and the textual descriptions in clinical narrative documents frequently indicate causal or posterior relationships that can be used to facilitate clinical named entity recognition. However, the CRF that is generally used for named entity recognition is a first-order model that constrains label transition dependency of adjoining labels under the Markov assumption.
methodsBased on the first-order structure, our proposed model utilizes non-entity tokens between separated entities as an information transmission medium by applying a label induction method. The model is referred to as precursor-induced CRF because its non-entity state memorizes precursor entity information, and the model's structure allows the precursor entity information to propagate forward through the label sequence.
resultsWe compared the proposed model with both first- and second-order CRFs in terms of their F
conclusionThe proposed precursor-induced CRF which uses non-entity labels as label transition information improves entity recognition F
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