ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2008
A prototype system to support evidence-based practice.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Review
- A customizable deep learning model for nosocomial risk prediction from critical care notes with indirect supervision.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Deep Learning from Incomplete Data: Detecting Imminent Risk of Hospital-acquired Pneumonia in ICU Patients.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Semantic annotation in biomedicine: the current landscape.Journal of biomedical semantics · 2017Review
- Automated confidence ranked classification of randomized controlled trial articles: an aid to evidence-based medicine.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Intelligent personal health record: experience and open issues.Journal of medical systems · 2012Article
- eEvidence: Information Seeking Support for Evidence-based Practice: An Implementation Case Study.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2010Article
- What can natural language processing do for clinical decision support?Journal of biomedical informatics · 2009Review
- Extracting Rx information from clinical narrative.Journal of the American Medical Informatics Association : JAMIAArticle
- Evaluation of a generalizable approach to clinical information retrieval using the automated retrieval console (ARC).Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Translating evidence into clinical practice is a complex process that depends on the availability of evidence, the environment into which the research evidence is translated, and the system that facilitates the translation. This paper presents InfoBot, a system designed for automatic delivery of patient-specific information from evidence-based resources. A prototype system has been implemented to support development of individualized patient care plans. The prototype explores possibilities to automatically extract patients problems from the interdisciplinary team notes and query evidence-based resources using the extracted terms. Using 4,335 de-identified interdisciplinary team notes for 525 patients, the system automatically extracted biomedical terminology from 4,219 notes and linked resources to 260 patient records. Sixty of those records (15 each for Pediatrics, Oncology & Hematology, Medical & Surgical, and Behavioral Health units) have been selected for an ongoing evaluation of the quality of automatically proactively delivered evidence and its usefulness in development of care plans.
Indexed as
Identifiers
18998835PMC2656073What 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.