ArticleScientific reports2019
Predictive Big Data Analytics using the UK Biobank Data.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 27 citations in OpenAlex.
- When genomics companies act as biobanks: governance challenges in an era of genomic assetization.Frontiers in genetics · 2026Article
- Revisiting the role of computational neuroimaging in the era of integrative neuroscience.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Best practices for data management and sharing in experimental biomedical research.Physiological reviews · 2024Review
- Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature.Applied clinical informatics · 2022Review
- Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review.Journal of asthma and allergy · 2022Review
- Modeling and prediction of pressure injury in hospitalized patients using artificial intelligence.BMC medical informatics and decision making · 2021Article
- [Artificial intelligence in neurocritical care].Der Nervenarzt · 2021Review
- Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression.NeuroImage · 2020Article
- Modernizing the Methods and Analytics Curricula for Health Science Doctoral Programs.Frontiers in public health · 2020Article
- Compressive Big Data Analytics: An ensemble meta-algorithm for high-dimensional multisource datasets.PloS one · 2020Article
- Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks.Brain topography · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
The UK Biobank is a rich national health resource that provides enormous opportunities for international researchers to examine, model, and analyze census-like multisource healthcare data. The archive presents several challenges related to aggregation and harmonization of complex data elements, feature heterogeneity and salience, and health analytics. Using 7,614 imaging, clinical, and phenotypic features of 9,914 subjects we performed deep computed phenotyping using unsupervised clustering and derived two distinct sub-cohorts. Using parametric and nonparametric tests, we determined the top 20 most salient features contributing to the cluster separation. Our approach generated decision rules to predict the presence and progression of depression or other mental illnesses by jointly representing and modeling the significant clinical and demographic variables along with the derived salient neuroimaging features. We reported consistency and reliability measures of the derived computed phenotypes and the top salient imaging biomarkers that contributed to the unsupervised clustering. This clinical decision support system identified and utilized holistically the most critical biomarkers for predicting mental health, e.g., depression. External validation of this technique on different populations may lead to reducing healthcare expenses and improving the processes of diagnosis, forecasting, and tracking of normal and pathological aging.
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.