Large-scale multi-modal information on patients’ health is ever increasing, providing an opportunity to use big data for taking individualized medicine to a global scale.
Focus on Big Data in Health
Big data is changing the face of medical research at a staggering pace. In this Focus issue, Nature Medicine explores Big Data: what are the next steps toward a substantial positive impact on human health?
Global health organization seeks to vet digital tools as it does vaccines and drugs.
A decade after its launch, H3Africa seeks to grow its local bioinformatics capacity.
Apple, Google and Samsung are partnering with academic researchers in new ways to leverage data from watches and smartphones.
Just how big is big data? The numbers may surprise you.
From asthma inhalers to blood-pressure monitors, companies with high-tech approaches are amassing and analyzing giant new data sources.
A statistical model based on an analysis of routinely collected data from 1980 to 2017 predicts 1,601 excess injury deaths per year in the contiguous USA if average temperatures rise by 1.5 °C.
Children at a higher risk of lead exposure develop smaller brain cortical surface area and volume, but only if they are from low-income families.
Review and Comment
Health data are being generated and collected at an unprecedented scale, but whether big data will truly revolutionize healthcare is still a matter of much debate.
Thinking the e-mail was a system error, she almost didn’t learn that her genetic test result had been revised. With the advent of commercial genomic screening, who is ethically responsible for communicating variant reclassification?
Healthcare is an imperfect practice, with disparities in care reflecting those in society. While algorithms may be misued to amplify biases, they may also be used to identify and correct disparities.
Big Data may revolutionize social science—and also amplify our deepest cultural biases.
Although examples of algorithms designed to improve healthcare delivery abound, for many, clinical integration will not be achieved. The deployment cost of machine learning models is an underappreciated barrier to success. Experts propose three criteria that, assessed early, could help estimate the deployment cost.
Among the many promises of big data, one of the most exciting could be the potential to unlock the detection of cancer before advanced malignancy ensures, which means opening up a whole new understanding of the disease.
The increased amount of health care data collected brings with it ethical and legal challenges for protecting the patient while optimizing health care and research.
Bayesian spatio-temporal modeling of mortality from injuries in the contiguous USA shows increases in the number of deaths attributable to abnormal temperature fluctuations due to global heating.
Leveraging the availability of nationwide electronic health records from over 500,000 pregnancies in Israel, a machine-learning approach offers an alternative means of predicting gestational diabetes at high accuracy in the early stages of pregnancy.
Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization
A retrospective analysis of existing computed tomography scans shows the feasibility of an automated process for evaluating osteoporotic fracture risk that could be used as an initial screening tool when FRAX inputs are unavailable.
Cross-sectional analysis of data from the Adolescent Brain Cognitive Development Study shows that children from families with low income are at increased risk of cognitive impairment associated with high lead-exposure risk when compared with children from families with high income.
Phenome-based approach identifies RIC1-linked Mendelian syndrome through zebrafish models, biobank associations and clinical studies
Integrated use of an animal model, a biobank for common diseases and a rare Mendelian disease leads to the discovery of a new syndrome and its pathological mechanism.
Integration of epidemiologic, pharmacologic, genetic and gut microbiome data in a drug–metabolite atlas
Comprising data from over 18,000 people, a new atlas of drug–metabolite associations for 87 commonly prescribed drugs and 150 metabolites assessed by proton nuclear magnetic resonance provides a web-based tool to aid research on drug efficacy, safety and repurposing.