ReviewPersonalized medicine2018
Data integration strategies for predictive analytics in precision medicine.
Review in Personalized medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Medical Big Data Storage in Precision Medicine: A Systematic Review.Journal of biomedical physics & engineering · 2025Article
- Design and development of a disease-specific clinical database system to increase the availability of hospital data in China.Health information science and systems · 2023Article
- Metabolomic profiling reveals extensive adrenal suppression due to inhaled corticosteroid therapy in asthma.Nature medicine · 2022Article
- Informatics Ecosystems to Advance the Biology of Glycans.Methods in molecular biology (Clifton, N.J.) · 2022Article
- MALDI-MSI as a Complementary Diagnostic Tool in Cytopathology: A Pilot Study for the Characterization of Thyroid Nodules.Cancers · 2019Article
- Article
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
With the rapid growth of health-related data including genomic, proteomic, imaging and clinical, the arduous task of data integration can be overwhelmed by the complexity of the environment including data size and diversity. This report examines the role of data integration strategies for big data predictive analytics in precision medicine research. Infrastructure-as-code methodologies will be discussed as a means of integrating and managing data. This includes a discussion on how and when these strategies can be used to lower barriers and address issues of consistency and interoperability within medical research environments. The goal is to support translational research and enable healthcare organizations to integrate and utilize infrastructure to accelerate the adoption of precision medicine.
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.