ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2016
Differentiating Sense through Semantic Interaction Data.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2016. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
- Natural language processing to assess the epidemiology of delirium-suggestive behavioural disturbances in critically ill patients.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2021Article
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
Words which have different representations but are semantically related, such as dementia and delirium, can pose difficult issues in understanding text. We explore the use of interaction frequency data between semantic elements as a means to differentiate concept pairs, using semantic predications extracted from the biomedical literature. We applied datasets of features drawn from semantic predications for semantically related pairs to two Expectation Maximization clustering processes (without, and with concept labels), then used all data to train and evaluate several concept classifying algorithms. For the unlabeled datasets, 80% displayed expected cluster count and similar or matching proportions; all labeled data exhibited similar or matching proportions when restricting cluster count to unique labels. The highest performing classifier achieved 89% accuracy, with F1 scores for individual concept classification ranging from 0.69 to 1. We conclude with a discussion on how these findings may be applied to natural language processing of clinical text.
Indexed as
Identifiers
28269921PMC5333208What 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.