Clinical data describing the phenotypes and treatment of patients represents an underused
data source that has much greater research potential than is currently realized. Mining of …

Aims Cohorts of millions of people's health records, whole genome sequencing, imaging,
sensor, societal and publicly available data present a rapidly expanding digital trace of …

Real-world evidence (RWE) and randomized control trial (RCT) data are considered
mutually complementary. However, compared with RCT, the outcomes of RWE continue to …

The French health care system is based on universal coverage by one of several health care
insurance plans. The SNIIRAM database merges anonymous information of reimbursed …

Background Non-alcoholic fatty liver disease (NAFLD) is the most common cause of liver
disease worldwide. It affects an estimated 20% of the general population, based on cohort …

Objectives To evaluate the utility of applying the Observational Medical Outcomes
Partnership (OMOP) Common Data Model (CDM) across multiple observational databases …

An important goal of the health system is to identify new adverse drug events (ADEs) in the
postapproval period. Data‐mining methods that can transform data into meaningful …

The discovery and quantification of adverse drug reactions has long relied on the careful
analysis of spontaneously reported cases. Causality assessment (imputation) was a …

Accurate prediction of disease risk based on the genetic make-up of an individual is
essential for effective prevention and personalized treatment. Nevertheless, to date …

Text mining is the computational process of extracting meaningful information from large
amounts of unstructured text. It is emerging as a tool to leverage underutilized data sources …