ReviewTheScientificWorldJournal2013
Biomedical informatics for computer-aided decision support systems: a survey.
Review in TheScientificWorldJournal, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial.The Lancet. Child & adolescent health · 2020Trial
- Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness.NeuroImage. Clinical · 2014Trial
- Moral diversity and the challenge of responsibility in AI-CDSS.Philosophy, ethics, and humanities in medicine : PEHM · 2026Article
- Prediction of the occurrence of leprosy reactions based on Bayesian networks.Frontiers in medicine · 2023Article
- Utilization of Artificial Intelligence in Disease Prevention: Diagnosis, Treatment, and Implications for the Healthcare Workforce.Healthcare (Basel, Switzerland) · 2022Review
- Five Priority Areas for Improving Medications Development for Alcohol Use Disorder and Promoting Their Routine Use in Clinical Practice.Alcoholism, clinical and experimental research · 2020Review
- Machine learning in critical care: state-of-the-art and a sepsis case study.Biomedical engineering online · 2018Observational
- Opportunities and obstacles for deep learning in biology and medicine.Journal of the Royal Society, Interface · 2018Review
- Personalization and Patient Involvement in Decision Support Systems: Current Trends.Yearbook of medical informatics · 2015Review
- Big Data Analytics in Healthcare.BioMed research international · 2015Review
- Semantic interpretation of robust imaging features for Fuhrman grading of renal carcinoma.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2014Article
- Ratsnake: a versatile image annotation tool with application to computer-aided diagnosis.TheScientificWorldJournal · 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 volumes of current patient data as well as their complexity make clinical decision making more challenging than ever for physicians and other care givers. This situation calls for the use of biomedical informatics methods to process data and form recommendations and/or predictions to assist such decision makers. The design, implementation, and use of biomedical informatics systems in the form of computer-aided decision support have become essential and widely used over the last two decades. This paper provides a brief review of such systems, their application protocols and methodologies, and the future challenges and directions they suggest.
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.