ReviewDialogues in clinical neuroscience2016
Transforming big data into computational models for personalized medicine and health care.
Review in Dialogues in clinical neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Narrative Review of Digital Twins in the Health Domain: Development, Application, and Evidence Consolidation.Medical sciences (Basel, Switzerland) · 2026Review
- Big data for neuroscience in the context of predictive, preventive, and personalized medicine.The EPMA journal · 2025Review
- Prediction of arabica coffee production using artificial neural network and multiple linear regression techniques.Scientific reports · 2022Article
- A nomogram with enhanced function facilitated by nomogramEx and nomogramFormula.Annals of translational medicine · 2020Article
- OPTIK: a database for understanding catchment areas to guide mobilization of cancer center assets.Database : the journal of biological databases and curation · 2020Article
- In-depth mining of clinical data: the construction of clinical prediction model with R.Annals of translational medicine · 2019Article
- Dynamical systems approaches to personalized medicine.Current opinion in biotechnology · 2019Review
- A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining.Healthcare (Basel, Switzerland) · 2018Review
- Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy.Kidney diseases (Basel, Switzerland) · 2018Review
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
Health care systems generate a huge volume of different types of data. Due to the complexity and challenges inherent in studying medical information, it is not yet possible to create a comprehensive model capable of considering all the aspects of health care systems. There are different points of view regarding what the most efficient approaches toward utilization of this data would be. In this paper, we describe the potential role of big data approaches in improving health care systems and review the most common challenges facing the utilization of health care big data.
Indexed as
Identifiers
27757067PMC5067150What 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.