ArticleBriefings in bioinformatics2017
Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams.
Article in Briefings in bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.
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
55 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Article
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- SciLinker: a large-scale text mining framework for mapping associations among biological entities.Frontiers in artificial intelligence · 2025Article
- Blockchain Technology in Digital Health and Medical Technologies.Blockchain in healthcare today · 2025Article
- Pharmacist-Led Digital Health Interventions to Improve Treatment Outcomes in Patients with Hypertension - A Systematic Review.Journal of multidisciplinary healthcare · 2025Review
- Circadian de(regulation) in physiology: implications for disease and treatment.Genes & development · 2024Review
- MSFC: a new feature construction method for accurate diagnosis of mass spectrometry data.Scientific reports · 2023Article
- Establishment and validation of an AI-aid method in the diagnosis of myocardial perfusion imaging.BMC medical imaging · 2023Article
- Challenges and Burdens in the Coronary Artery Disease Care Pathway for Patients Undergoing Percutaneous Coronary Intervention: A Contemporary Narrative Review.International journal of environmental research and public health · 2023Review
- Review
- Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study.Brain informatics · 2022Article
- A Novel Intelligent Hybrid Optimized Analytics and Streaming Engine for Medical Big Data.Computational and mathematical methods in medicine · 2022Article
- Accommodating Serial Correlation and Sequential Design Elements in Personalized Studies and Aggregated Personalized Studies.Harvard data science review · 2022Article
- Gut microbial determinants of clinically important improvement in patients with rheumatoid arthritis.Genome medicine · 2021Article
- OMICS approaches in cardiovascular diseases: a mini review.Genomics & informatics · 2021Article
- Predictive Modelling of Susceptibility to Substance Abuse, Mortality and Drug-Drug Interactions in Opioid Patients.Frontiers in artificial intelligence · 2021Article
- Translational Informatics for Natural Products as Antidepressant Agents.Frontiers in cell and developmental biology · 2021Review
- Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning.Journal of surgical oncology · 2021Review
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
Monitoring and modeling biomedical, health care and wellness data from individuals and converging data on a population scale have tremendous potential to improve understanding of the transition to the healthy state of human physiology to disease setting. Wellness monitoring devices and companion software applications capable of generating alerts and sharing data with health care providers or social networks are now available. The accessibility and clinical utility of such data for disease or wellness research are currently limited. Designing methods for streaming data capture, real-time data aggregation, machine learning, predictive analytics and visualization solutions to integrate wellness or health monitoring data elements with the electronic medical records (EMRs) maintained by health care providers permits better utilization. Integration of population-scale biomedical, health care and wellness data would help to stratify patients for active health management and to understand clinically asymptomatic patients and underlying illness trajectories. In this article, we discuss various health-monitoring devices, their ability to capture the unique state of health represented in a patient and their application in individualized diagnostics, prognosis, clinical or wellness intervention. We also discuss examples of translational bioinformatics approaches to integrating patient-generated data with existing EMRs, personal health records, patient portals and clinical data repositories. Briefly, translational bioinformatics methods, tools and resources are at the center of these advances in implementing real-time biomedical and health care analytics in the clinical setting. Furthermore, these advances are poised to play a significant role in clinical decision-making and implementation of data-driven medicine and wellness care.
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.