Juan M. Banda

TL;DR: An international data network with 11 data sources from four countries, including electronic health records and administrative claims data on 250 million patients revealed similar results, suggesting large-scale international observational research is feasible.

TL;DR: The authors presented a large-scale curated dataset of over 152 million tweets, growing daily, related to COVID-19 chatter generated from January 1st to April 4th at the time of writing, allowing researchers to conduct a number of research projects relating to the emotional and mental responses to social distancing measures, the identification of sources of misinformation, and the stratified measurement of sentiment towards the pandemic in near real time.

TL;DR: A large-scale curated dataset of over 152 million tweets, growing daily, related to COVID-19 chatter generated from January 1st to April 4th at the time of writing, will allow researchers to conduct a number of research projects relating to the emotional and mental responses to social distancing measures and the identification of sources of misinformation.

TL;DR: A review of the evolution of electronic phenotyping, from the early rule-based methods to the cutting edge of supervised and unsupervised machine learning models, with a focus on both methodology and implementation.

TL;DR: The feasibility of utilizing semi-automatically labeled training sets to create phenotype models via machine learning, using a comprehensive representation of the patient medical record is demonstrated, providing an alternative to manual labeling for creating training sets for statistical models of phenotypes.