ArticleProceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data2014
Empowering Personalized Medicine with Big Data and Semantic Web Technology: Promises, Challenges, and Use Cases.
Article in Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis 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
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A meta-analysis of the relationship between anxiety and non-suicidal self-injury based on knowledge graphs.Frontiers in psychiatry · 2024Pooled it
- Smartphone Application WeChat for Clinical Follow-up of Discharged Patients with Head and Neck Tumors: A Randomized Controlled Trial.Chinese medical journal · 2016Trial
- Predicting individual hemoglobin abnormalities using longitudinal data in clinical practice.BMC medical informatics and decision making · 2025Article
- Article
- Knowledge graph and its application in the study of neurological and mental disorders.Frontiers in psychiatry · 2025Review
- ADHD-KG: a knowledge graph of attention deficit hyperactivity disorder.Health information science and systems · 2023Article
- Harmonizing Progress:: Bridging BioPharma, Technology, Academia, and Healthcare for Advanced Drug Manufacturing.Delaware journal of public health · 2023Article
- Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing.Scientific reports · 2022Article
- Worldwide mapping of initiatives that integrate population cohorts.Frontiers in public health · 2022Article
- Tackling the translational challenges of multi-omics research in the realm of European personalised medicine: A workshop report.Frontiers in molecular biosciences · 2022Article
- Clinical decision support systems in orthodontics: A narrative review of data science approaches.Orthodontics & craniofacial research · 2021Review
- Knowledge Graphs of Kawasaki Disease.Health information science and systems · 2021Article
- Impact of Big Data Analytics on People's Health: Overview of Systematic Reviews and Recommendations for Future Studies.Journal of medical Internet research · 2021Article
- Data Integration Challenges for Machine Learning in Precision Medicine.Frontiers in medicine · 2021Review
- Big Data and the Study of Social Inequalities in Health: Expectations and Issues.Frontiers in public health · 2018Article
- Reporting Data Quality Assessment Results: Identifying Individual and Organizational Barriers and Solutions.EGEMS (Washington, DC) · 2017Article
- Harnessing Big Data for Systems Pharmacology.Annual review of pharmacology and toxicology · 2017Review
- Big genomics and clinical data analytics strategies for precision cancer prognosis.Scientific reports · 2016Article
- [Freiburg keratoconus registry : Example of application of smart data for clinical research and inititial results].Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft · 2016Article
- Using e-technologies in clinical trials.Contemporary clinical trials · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
In healthcare, big data tools and technologies have the potential to create significant value by improving outcomes while lowering costs for each individual patient. Diagnostic images, genetic test results and biometric information are increasingly generated and stored in electronic health records presenting us with challenges in data that is by nature high volume, variety and velocity, thereby necessitating novel ways to store, manage and process big data. This presents an urgent need to develop new, scalable and expandable big data infrastructure and analytical methods that can enable healthcare providers access knowledge for the individual patient, yielding better decisions and outcomes. In this paper, we briefly discuss the nature of big data and the role of semantic web and data analysis for generating "smart data" which offer actionable information that supports better decision for personalized medicine. In our view, the biggest challenge is to create a system that makes big data robust and smart for healthcare providers and patients that can lead to more effective clinical decision-making, improved health outcomes, and ultimately, managing the healthcare costs. We highlight some of the challenges in using big data and propose the need for a semantic data-driven environment to address them. We illustrate our vision with practical use cases, and discuss a path for empowering personalized medicine using big data and semantic web technology.
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.