Topic List : Big Data
EHRs for Fido
A team led by scientists at the School of Medicine has developed an algorithm that can read the typed-out notes from veterinarians and predict specific diseases that the animal may have.
Farming linked to gut microbiome changes
Researchers at Stanford and several other institutions have linked the gut ecosystems of four Himalayan groups to the extent of each group’s departure from a hunter-gatherer lifestyle.
Older dads linked to higher birth risks
From the data of more than 40 million births, scientists at Stanford have linked paternal age to birth risks, and even risks to the mother’s health.
Symposium addresses electronic health records
The daylong event touched on fixing inefficiencies in EHRs, harnessing data for population health management, building on successes and overcoming obstacles.
Annual conference focuses on ‘treasure troves of data’
Dozens of speakers gathered at Stanford to discuss health, artificial intelligence and evolving technology and how it all could affect patient care at the annual Big Data in Precision Health conference.
Examining value of predictive algorithms
Big-data analysts engaged in lively debate on machine learning strategies at a colloquium organized by the Division of Epidemiology.
Big data conference set for May 23-24
The two-day conference will feature leaders from academia, government and industry who harness immense data sets to more precisely predict, diagnose and treat disease.
Weight flux alters molecular profile
Stanford scientists have found links between changes in a person’s weight and shifts in their microbiome, immune system and cardiovascular system.
Predicting, preventing a second stroke
Using health records, Stanford researchers developed an algorithm for scoring the risk of a stroke patient experiencing a heart condition known as atrial fibrillation, a major risk factor for a second stroke.
Stanford launches health care trends report
The inaugural issue of the report shows that big data will transform health care in the future but that more needs to be done to train doctors and patients in data management and analysis.