A comprehensive comparison of the two types of pancreatic cancer symptom trajectories can in combination leverage a fuller potential of large health data and ultimately aid the detection of early risk factors for pancreatic cancer.

Applying deep learning technology for the large-scale curation of symptoms from unstructured EHR clinical notes accurately predicts the differential signals of COVID-19 diagnosis over the week preceding typical PCR testing.

Text mining of complete EHRs for 14,017 diabetes patients and subsequent clustering led to phenotypically deep clusters, showing distinct glycemic profiles, comorbidities, and SNP association patterns.

Large-scale retrospective analysis suggests medications and dietary supplements, such as rosuvastatin, ezetimibe, ubiquinone, risedronate, vitamin D, and magnesium, are associated with a lower rate of severe COVID-19 disease.

An integrative approach, combining genetic mouse and large-scale human genetics studies, was used to reveal a novel role for the Bcl-2 protein Bid in maintenance of mitochondrial function that alters susceptibility to myocardial infarction.

There was substantial change in primary care activity following the onset of the COVID-19 pandemic in March 2019 as indicated by key measures reflective of clinical activity in general practice but service was largely restored by April 2021.

A common genetic variant in Black patients increases the risk of renal disease, which increases the risk of sepsis, but there is no direct association between the variant and sepsis.

Computational methods were used to develop accurate manual scorecards for early detection of participants at risk of type 2 diabetes based on the UK Biobank database.

The LOTUS initiative builds, through the Wikidata knowledge graph, a virtuous cycle of data sharing practices for natural products research.

Longitudinal laboratory testing results tied to SARS-CoV-2 diagnostic PCR results of more than 2500 patients reveal the temporal evolution of COVID-associated coagulopathy.