ReviewJMIR medical informatics2014
Big data and clinicians: a review on the state of the science.
Review 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 51 papers, 3 of them syntheses 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
51 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Health-Related Data Sources Accessible to Health Researchers From the US Government: Mapping Review.Journal of medical Internet research · 2023Pooled it
- Outcomes after bariatric surgery according to large databases: a systematic review.Langenbeck's archives of surgery · 2017Pooled it
- Is real world evidence influencing practice? A systematic review of CPRD research in NICE guidances.BMC health services research · 2016Pooled it
- Deconstructing the Interoperability Stack: A Comparative Technical Analysis of HL7 v2, CDA, and the FHIR Resource Framework.Computational and structural biotechnology journal · 2026Review
- Resources and applications of public biomedical data.Frontiers in bioinformatics · 2026Review
- Machine learning-based diagnostic models for prevalent metabolic syndrome among patients with disuse muscle atrophy: a comparative study of seven algorithms.Frontiers in endocrinology · 2026Article
- Large Language Model Versus Manual Review for Clinical Data Curation in Breast Cancer: Retrospective Comparative Study.JMIR medical informatics · 2025Article
- Cancer chemoprevention: signaling pathways and strategic approaches.Signal transduction and targeted therapy · 2025Review
- MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.Frontiers in genetics · 2024Article
- A New Fuzzy-Based Classification Method for Use in Smart/Precision Medicine.Bioengineering (Basel, Switzerland) · 2023Article
- Improving child health through Big Data and data science.Pediatric research · 2023Review
- TLSEA: a tool for lncRNA set enrichment analysis based on multi-source heterogeneous information fusion.Frontiers in genetics · 2023Article
- Perspective Toward Machine Learning Implementation in Pediatric Medicine: Mixed Methods Study.JMIR medical informatics · 2022Article
- Integrated multimodal artificial intelligence framework for healthcare applications.NPJ digital medicine · 2022Article
- The role of big data analysis in identifying a relationship between glaucoma and diabetes mellitus.Annals of translational medicine · 2022Article
- Characterization of Traumatic Injury During the Early COVID-19 Pandemic: Results From a National Healthcare Database.Cureus · 2022Article
- Big Data Health Care Platform With Multisource Heterogeneous Data Integration and Massive High-Dimensional Data Governance for Large Hospitals: Design, Development, and Application.JMIR medical informatics · 2022Article
- Big data in the healthcare system: a synergy with artificial intelligence and blockchain technology.Journal of integrative bioinformatics · 2021Review
- The social licence for data-intensive health research: towards co-creation, public value and trust.BMC medical ethics · 2021Review
- Microbial natural product databases: moving forward in the multi-omics era.Natural product reports · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn the past few decades, medically related data collection saw a huge increase, referred to as big data. These huge datasets bring challenges in storage, processing, and analysis. In clinical medicine, big data is expected to play an important role in identifying causality of patient symptoms, in predicting hazards of disease incidence or reoccurrence, and in improving primary-care quality.
objectiveThe objective of this review was to provide an overview of the features of clinical big data, describe a few commonly employed computational algorithms, statistical methods, and software toolkits for data manipulation and analysis, and discuss the challenges and limitations in this realm.
methodsWe conducted a literature review to identify studies on big data in medicine, especially clinical medicine. We used different combinations of keywords to search PubMed, Science Direct, Web of Knowledge, and Google Scholar for literature of interest from the past 10 years.
resultsThis paper reviewed studies that analyzed clinical big data and discussed issues related to storage and analysis of this type of data.
conclusionsBig data is becoming a common feature of biological and clinical studies. Researchers who use clinical big data face multiple challenges, and the data itself has limitations. It is imperative that methodologies for data analysis keep pace with our ability to collect and store data.
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.