A Stanford researcher has found that patients with heart failure, even if it's relatively mild, are more likely to die within three months after surgery.
A Stanford study highlights a data optimization method for health-risk assessments to lower costs and and improve diagnostic power.
The latest issue of Stanford Medicine magazine explores the potential for digitally driven innovation to transform health education, diagnostics and care.
A Stanford team has developed an algorithm that uses data about tumors to identify new classifications that can provide information about patient outcomes
A Stanford study examines a key aspect of artificial intelligence: If machines provide advice for patient care, who should those machines be learning from?
A new white paper from Stanford Medicine details obstacles and offers solutions for achieving the full potential of electronic health records.
Scientists have developed an algorithm that combines genome sequence data and electronic health information to predict risk for genetic disease.
Citizen science through an online computer game, EVE online, helps scientists better classify protein locations inside a cell.
Video interviews from Stanford's Big Data in Precision Health conference explore topics from artificial intelligence in radiology to clinical informatics.
Stanford Medicine's Electronic Health Records National Symposium touched on improving inefficiencies of EHRs, harnessing data for population health management, building on successes and overcoming obstacles.
Most participants in clinical trials believe the benefits of broadly sharing individual data outweigh the risks, a new Stanford study has found.
At a time when technology is bringing the world closer together, the practice and potential of sharing patient data is beginning to blur the notion of “rare” diseases, and offer more options for identifying and treating conditions previously considered undiagnosed, panelists at a Stanford conference said.
During a digital health-focused session at the Big Data in Precision Health conference, four speakers detailed the ways in which they're harnessing digital technologies to empower patient health.
Experts from academia, industry government and more came together at this year's Big Data in Precision Health conference to share successes, lessons and insights into how they're breaking down data to precisely advance health care and research.
Dekel Gelbman, CEO of FDNA, speaks on the role of artificial intelligence in health care, and how he sees AI contributing to genetic diagnostic in particular.
Jenna Wiens, an assistant professor at the University of Michigan, speaks to how big data, machine learning and health care intersect in advance of the Big Data in Precision Health conference at Stanford.