ArticleJMIR medical informatics2014
Dynamic clinical data mining: search engine-based decision support.
Article in JMIR medical informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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.
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Who cites it
16 citing papers in PubMed.
- Thinking forward: promising but unproven ideas for future intensive care.Critical care (London, England) · 2019Article
- An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support.JMIR mental health · 2019Article
- Critical Care, Critical Data.Biomedical engineering and computational biology · 2019Review
- Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma.PloS one · 2019Article
- A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining.Healthcare (Basel, Switzerland) · 2018Review
- Dynamic Clinical Algorithms: Digital Technology Can Transform Health Care Decision-Making.The American journal of tropical medicine and hygiene · 2018Article
- Computerized Decision Aids for Shared Decision Making in Serious Illness: Systematic Review.JMIR medical informatics · 2017Article
- Clinical Note Creation, Binning, and Artificial Intelligence.JMIR medical informatics · 2017Article
- Ecological Assessment of Clinicians' Antipsychotic Prescription Habits in Psychiatric Inpatients: A Novel Web- and Mobile Phone-Based Prototype for a Dynamic Clinical Decision Support System.Journal of medical Internet research · 2017Observational
- Using electronic health record collected clinical variables to predict medical intensive care unit mortality.Annals of medicine and surgery (2012) · 2016Article
- Disrupting Electronic Health Records Systems: The Next Generation.JMIR medical informatics · 2015Article
- Engineering control into medicine.Journal of critical care · 2015Review
- Prioritization of free-text clinical documents: a novel use of a bayesian classifier.JMIR medical informatics · 2015Article
- State of the art review: the data revolution in critical care.Critical care (London, England) · 2015Review
- Optimal data systems: the future of clinical predictions and decision support.Current opinion in critical care · 2014Review
- From Pharmacovigilance to Clinical Care Optimization.Big data · 2014Article
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
No grant is acknowledged in the PubMed record.
Abstract
The research world is undergoing a transformation into one in which data, on massive levels, is freely shared. In the clinical world, the capture of data on a consistent basis has only recently begun. We propose an operational vision for a digitally based care system that incorporates data-based clinical decision making. The system would aggregate individual patient electronic medical data in the course of care; query a universal, de-identified clinical database using modified search engine technology in real time; identify prior cases of sufficient similarity as to be instructive to the case at hand; and populate the individual patient's electronic medical record with pertinent decision support material such as suggested interventions and prognosis, based on prior outcomes. Every individual's course, including subsequent outcomes, would then further populate the population database to create a feedback loop to benefit the care of future patients.
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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.