ArticleBMC medical informatics and decision making2012
ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials.
Article in BMC medical informatics and decision making, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed.
- Collaborative, Multidisciplinary Evaluation of Cancer Variants Through Virtual Molecular Tumor Boards Informs Local Clinical Practices.JCO clinical cancer informatics · 2020Review
- Automatic classification of registered clinical trials towards the Global Burden of Diseases taxonomy of diseases and injuries.BMC bioinformatics · 2016Article
- Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Adaptive semantic tag mining from heterogeneous clinical research texts.Methods of information in medicine · 2015Article
- Clustering clinical trials with similar eligibility criteria features.Journal of biomedical informatics · 2014Article
- ClinicalTrials.gov as a data source for semi-automated point-of-care trial eligibility screening.PloS one · 2014Article
- Unsupervised mining of frequent tags for clinical eligibility text indexing.Journal of biomedical informatics · 2013Article
- eTACTS: a method for dynamically filtering clinical trial search results.Journal of biomedical informatics · 2013Article
- Open issues in intelligent personal health record--an updated status report for 2012.Journal of medical systems · 2013Article
- A method for discovering and inferring appropriate eligibility criteria in clinical trial protocols without labeled data.BMC medical informatics and decision making · 2013Article
- Feasibility of feature-based indexing, clustering, and search of clinical trials. A case study of breast cancer trials from ClinicalTrials.gov.Methods of information in medicine · 2013Article
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
Clinical trials are mandatory protocols describing medical research on humans and among the most valuable sources of medical practice evidence. Searching for trials relevant to some query is laborious due to the immense number of existing protocols. Apart from search, writing new trials includes composing detailed eligibility criteria, which might be time-consuming, especially for new researchers. In this paper we present ASCOT, an efficient search application customised for clinical trials. ASCOT uses text mining and data mining methods to enrich clinical trials with metadata, that in turn serve as effective tools to narrow down search. In addition, ASCOT integrates a component for recommending eligibility criteria based on a set of selected protocols.
Indexed as
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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.