ReviewGigaScience2016
Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data.
Review in GigaScience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 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
42 citing papers in PubMed.
- Artificial intelligence in atopic dermatitis: Current applications and future perspectives.Chinese medical journal · 2026Review
- Resources and applications of public biomedical data.Frontiers in bioinformatics · 2026Review
- Cross-platform multi-cancer histopathology classification using local-window vision transformers.Scientific reports · 2025Article
- Correcting for the Inflated Adult Population Denominator in an English Nationwide Health Care Cohort: Database Analysis Study.JMIR public health and surveillance · 2025Article
- New implementation of data standards for AI in oncology: Experience from the EuCanImage project.GigaScience · 2025Article
- Advancing human health risk assessment: the role of new approach methodologies.Frontiers in toxicology · 2025Review
- A machine learning-based method for feature reduction of methylation data for the classification of cancer tissue origin.International journal of clinical oncology · 2024Article
- Evaluating algorithms of decision tree, support vector machine and regression for anode side catalyst data in proton exchange membrane water electrolysis.Scientific reports · 2023Article
- Generating synthetic mixed-type longitudinal electronic health records for artificial intelligent applications.NPJ digital medicine · 2023Article
- A Hybrid Stacked CNN and Residual Feedback GMDH-LSTM Deep Learning Model for Stroke Prediction Applied on Mobile AI Smart Hospital Platform.Sensors (Basel, Switzerland) · 2023Article
- Digital public health leadership in the global fight for health security.BMJ global health · 2023Article
- Leveraging Deep Learning for Designing Healthcare Analytics Heuristic for Diagnostics.Neural processing letters · 2023Article
- Investigation into theFrontiers in pharmacology · 2022Article
- Short-Term Event Prediction in the Operating Room (STEP-OP) of Five-Minute Intraoperative Hypotension Using Hybrid Deep Learning: Retrospective Observational Study and Model Development.JMIR medical informatics · 2021Article
- The concept of justifiable healthcare and how big data can help us to achieve it.BMC medical informatics and decision making · 2021Review
- Efficient Prediction of Missed Clinical Appointment Using Machine Learning.Computational and mathematical methods in medicine · 2021Article
- Validity of the Work Assessment Triage Tool for Selecting Rehabilitation Interventions for Workers' Compensation Claimants with Musculoskeletal Conditions.Journal of occupational rehabilitation · 2020Article
- Brief introduction of medical database and data mining technology in big data era.Journal of evidence-based medicine · 2020Review
- Modernizing the Methods and Analytics Curricula for Health Science Doctoral Programs.Frontiers in public health · 2020Article
- The future of sleep health: a data-driven revolution in sleep science and medicine.NPJ digital medicine · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Managing, processing and understanding big healthcare data is challenging, costly and demanding. Without a robust fundamental theory for representation, analysis and inference, a roadmap for uniform handling and analyzing of such complex data remains elusive. In this article, we outline various big data challenges, opportunities, modeling methods and software techniques for blending complex healthcare data, advanced analytic tools, and distributed scientific computing. Using imaging, genetic and healthcare data we provide examples of processing heterogeneous datasets using distributed cloud services, automated and semi-automated classification techniques, and open-science protocols. Despite substantial advances, new innovative technologies need to be developed that enhance, scale and optimize the management and processing of large, complex and heterogeneous data. Stakeholder investments in data acquisition, research and development, computational infrastructure and education will be critical to realize the huge potential of big data, to reap the expected information benefits and to build lasting knowledge assets. Multi-faceted proprietary, open-source, and community developments will be essential to enable broad, reliable, sustainable and efficient data-driven discovery and analytics. Big data will affect every sector of the economy and their hallmark will be 'team science'.
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.