Real World Data Versus Probability Surveys for Estimating Health Conditions at the State Level.

Government statistical offices worldwide are under pressure to produce statistics rapidly and for more detailed geographies, to compete with unofficial estimates available from web-based big data sources or from private companies. Commonly suggested sources of improved health information are electronic health records (EHRs) and medical claims data. These data sources are collectively known as real world data (RWD) because they are generated from routine health care processes, and they are available for millions of patients.

Estimation of prevalence of autoimmune diseases in the United States using electronic health record data.

Background: Previous epidemiologic studies of autoimmune diseases in the United States (US) have included a limited number of diseases or used meta-analyses that rely on different data collection methods and analyses for each disease.

Methods: To estimate the prevalence of autoimmune diseases in the US, we used electronic health record data from six large medical systems in the US. We developed a software program using common methodology to compute the estimated prevalence of autoimmune diseases alone and in aggregate that can be readily used by other investigators to replicate or modify the analysis over time.

Physiological signal analysis and open science using the Julia language and associated software.

In this mini review, we propose the use of the Julia programming language and its software as a strong candidate for reproducible, efficient, and sustainable physiological signal analysis. First, we highlight available software and Julia communities that provide top-of-the-class algorithms for all aspects of physiological signal processing despite the language's relatively young age. Julia can significantly accelerate both research and software development due to its high-level interactive language and high-performance code generation.