ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2017
Exploiting Unlabeled Texts with Clustering-based Instance Selection for Medical Relation Classification.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Development and Evaluation of a Natural Language Processing System for Curating a Trans-Thoracic Echocardiogram (TTE) Database.Bioengineering (Basel, Switzerland) · 2023Article
- A Hybrid Model for Family History Information Identification and Relation Extraction: Development and Evaluation of an End-to-End Information Extraction System.JMIR medical informatics · 2021Article
- Ensemble method-based extraction of medication and related information from clinical texts.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- RCorp: a resource for chemical disease semantic extraction in Chinese.BMC medical informatics and decision making · 2019Article
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
Classifying relations between pairs of medical concepts in clinical texts is a crucial task to acquire empirical evidence relevant to patient care. Due to limited labeled data and extremely unbalanced class distributions, medical relation classification systems struggle to achieve good performance on less common relation types, which capture valuable information that is important to identify. Our research aims to improve relation classification using weakly supervised learning. We present two clustering-based instance selection methods that acquire a diverse and balanced set of additional training instances from unlabeled data. The first method selects one representative instance from each cluster containing only unlabeled data. The second method selects a counterpart for each training instance using clusters containing both labeled and unlabeled data. These new instance selection methods for weakly supervised learning achieve substantial recall gains for the minority relation classes compared to supervised learning, while yielding comparable performance on the majority relation classes.
Indexed as
Identifiers
29854174PMC5977715What 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.