Meanwhile, similarity matching algorithms identify healthcare patients who need ... There are also data scientists who don't know SQL and who jumped onto the graph bandwagon, but their number ...

CLEVELAND, Ohio — Large databases of electronic medical records hold great promise for medical research. In theory they can provide doctors access to huge amounts of anonymous patient data ...

They had to figure out what happens if patients ... SQL-compatible comes with the territory. What is not understandable to us, however, is the lack of support for interoperability on the graph ...

Neo4j has been used by them for tasks such as patient management, drug research, clinical trials, genomics, and marketing. The health care industry is not alone in adopting graph databases—Neo4j ...

Graph databases differ from traditional SQL databases in that they store both data and the relationships among the data. The relationships among data points are as important as the data points ...

To deliver on this imperative, it’s important for them to understand the relationships among the members or patients and the ... Fortunately, the latest graph databases are designed to handle ...

They require complex, slow, and expensive work with large tables containing prescriber, claims and patient data. And while graph database technology makes uncovering referral relationships much ...

identifying early interventions for complicated patient journeys, and predicting fraud through sequences of seemingly innocuous behavior. The new framework combines a native graph analytics workspace ...

Graph database also plays an important role in the healthcare and life science sector, from recording patient information from various sources to discharging patients. For instance, healthcare ...