ArticleKidney research and clinical practice2017
Medical big data: promise and challenges.
Article in Kidney research and clinical practice, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05697198 (PRospective REgistry of Advanced Stage CancER), which is not on this map. Cited by 131 papers, 7 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.
PRospective REgistry of Advanced Stage CancER (PREFER) Patients to Assess Prevalence of Actionable Biomarkers and Driver Mutations Using the OmniSeq Test and Creation of a Biobank from Community Cancer Clinics in the United States to Address Disparities in Precision Medicine
Who cites it
131 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Ethical issues raised by artificial intelligence and big data in population health: a scoping review.Frontiers in sociology · 2025Pooled it
- A systematic review of predictive models for hospital-acquired pressure injury using machine learning.Nursing open · 2023Pooled it
- Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies.EBioMedicine · 2023Pooled it
- Pooled it
- Big Data and Digitalization in Dentistry: A Systematic Review of the Ethical Issues.International journal of environmental research and public health · 2020Pooled it
- Artificial Intelligence: A New Tool in Operating Room Management. Role of Machine Learning Models in Operating Room Optimization.Journal of medical systems · 2019Pooled it
- Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations.RMD open · 2019Pooled it
- Modeling dynamics and alternative treatment strategies in acute promyelocytic leukemia.PloS one · 2019Trial
- External Control Augmentation Increases Estimates Precision for Finerenone plus Sodium-Glucose Cotransporter-2 Inhibitors.Kidney international reports · 2026Article
- AI-driven integration of Framingham Heart Study data with machine learning, deep learning, and explainable AI for enhanced pharmaceutical marketing.Scientific reports · 2026Article
- Unifying to Advance Understanding: Collaborative, Community-Driven and 'Open' Approaches for Better Science in Sport.Sports medicine (Auckland, N.Z.) · 2026Review
- Using voice and speech data in healthcare: a scoping review of the ethical, legal and social implications.Frontiers in digital health · 2026Review
- Artificial Intelligence in The Diagnosis, Treatment, and Prognostication in Endodontics: A Comprehensive Literature Review.European endodontic journal · 2025Review
- Predicting child and adolescent mental health emergency department revisits: a machine-learning approach compared to a clinician-derived baseline.BMC medical informatics and decision making · 2025Article
- Trends in immunomodulatory therapy for the treatment of chronic uveitis in the United States.Journal of ophthalmic inflammation and infection · 2025Article
- Frameworks for describing health inequalities in observational public health research: a scoping review protocol.BMJ open · 2025Article
- Artificial Intelligence in Ocular Transcriptomics: Applications of Unsupervised and Supervised Learning.Cells · 2025Review
- Enhancing Clinical Data Infrastructure for AI Research: Comparative Evaluation of Data Management Architectures.Journal of medical Internet research · 2025Article
- A Methodological and Survival Comparison of NCDB and SEER Database for Colon Cancer Research.Journal of surgical oncology · 2025Article
- Revolutionizing Utility of Big Data Analytics in Personalized Cardiovascular Healthcare.Bioengineering (Basel, Switzerland) · 2025Review
71 more citing papers are in PubMed but not listed here.
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
The concept of big data, commonly characterized by volume, variety, velocity, and veracity, goes far beyond the data type and includes the aspects of data analysis, such as hypothesis-generating, rather than hypothesis-testing. Big data focuses on temporal stability of the association, rather than on causal relationship and underlying probability distribution assumptions are frequently not required. Medical big data as material to be analyzed has various features that are not only distinct from big data of other disciplines, but also distinct from traditional clinical epidemiology. Big data technology has many areas of application in healthcare, such as predictive modeling and clinical decision support, disease or safety surveillance, public health, and research. Big data analytics frequently exploits analytic methods developed in data mining, including classification, clustering, and regression. Medical big data analyses are complicated by many technical issues, such as missing values, curse of dimensionality, and bias control, and share the inherent limitations of observation study, namely the inability to test causality resulting from residual confounding and reverse causation. Recently, propensity score analysis and instrumental variable analysis have been introduced to overcome these limitations, and they have accomplished a great deal. Many challenges, such as the absence of evidence of practical benefits of big data, methodological issues including legal and ethical issues, and clinical integration and utility issues, must be overcome to realize the promise of medical big data as the fuel of a continuous learning healthcare system that will improve patient outcome and reduce waste in areas including nephrology.
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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.