ReviewOpen respiratory archives
[Respiratory Disease in the Era of Big Data].
Review in Open respiratory archives. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed, 1 citations in OpenAlex.
No citing paper in PubMed yet.
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the key elements of medicine in the second decade of the 21st century is the exponential growth of patient-produced information, due not only to the transition to the digitization of medical records, but also to the emergence of new sources of information and the capacity for analysis and interpretation of existing ones. The amount of medical information is expected to double every 2 years, which means that there will be 50 times more information available in 2020 than in 2011. In this setting, these large amounts of data or «big data» must be properly managed to implement new initiatives that improve the diagnosis, treatment, and prognosis of patients on the path to personalized medicine.The concept of personalization or precision medicine is of special interest in chronic respiratory disease. In recent years, research in entities such as asthma, COPD, cancer, or SAHS has focused on the identification of genomic, molecular, metabolic, and protein changes (biomarkers). Big data analysis tools can be used to move on from models based on the mean response to treatment, which are suboptimal for most patients, to focus on the individualized response. Part of this journey involves systems medicine, which also integrates clinical and population data to provide a multidimensional view of the disease and help identify causal associations that are usually only evident on big data analysis.
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.