ArticleProceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications2011
A textual representation scheme for identifying clinical relationships in patient records.
Article in Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- A context-blocks model for identifying clinical relationships in patient records.BMC bioinformatics · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The identification of relationships between clinical concepts in patient records is a preliminary step for many important applications in medical informatics, ranging from quality of care to hypothesis generation. In this work we describe an approach that facilitates the automatic recognition of relationships defined between two different concepts in text. Unlike the traditional bag-of-words representation, in this work, a relationship is represented with a scheme of five distinct context-blocks based on the position of concepts in the text. This scheme was applied to eight different relationships, between medical problems, treatments and tests, on a set of 349 patient records from the 4th i2b2 challenge. Results show that the context-block representation was very successful (F-Measure = 0.775) compared to the bag-of-words model (F-Measure = 0.402). The advantage of this representation scheme was the correct management of word position information, which may be critical in identifying certain relationships.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.