ArticleBMC medical informatics and decision making2005
Personalized online information search and visualization.
Article in BMC medical informatics and decision making, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Review of extracting information from the Social Web for health personalization.Journal of medical Internet research · 2011Review
- Linking genes to literature: text mining, information extraction, and retrieval applications for biology.Genome biology · 2008Review
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
backgroundThe rapid growth of online publications such as the Medline and other sources raises the questions how to get the relevant information efficiently. It is important, for a bench scientist, e.g., to monitor related publications constantly. It is also important, for a clinician, e.g., to access the patient records anywhere and anytime. Although time-consuming, this kind of searching procedure is usually similar and simple. Likely, it involves a search engine and a visualization interface. Different words or combination reflects different research topics. The objective of this study is to automate this tedious procedure by recording those words/terms in a database and online sources, and use the information for an automated search and retrieval. The retrieved information will be available anytime and anywhere through a secure web server.
resultsWe developed such a database that stored searching terms, journals and et al., and implement a piece of software for searching the medical subject heading-indexed sources such as the Medline and other online sources automatically. The returned information were stored locally, as is, on a server and visible through a Web-based interface. The search was performed daily or otherwise scheduled and the users logon to the website anytime without typing any words. The system has potentials to retrieve similarly from non-medical subject heading-indexed literature or a privileged information source such as a clinical information system. The issues such as security, presentation and visualization of the retrieved information were thus addressed. One of the presentation issues such as wireless access was also experimented. A user survey showed that the personalized online searches saved time and increased and relevancy. Handheld devices could also be used to access the stored information but less satisfactory.
conclusionThe Web-searching software or similar system has potential to be an efficient tool for both bench scientists and clinicians for their daily information needs.
Indexed as
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.