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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?
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.
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.
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.
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.
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.
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.
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.