Employing user-centered design and education sciences to inform training of diabetes survival skills.

Background: Patients newly diagnosed with diabetes mellitus (diabetes), who require insulin must acquire diabetes "survival" skills prior to discharge home. COVID-19 revealed considerable limitations of traditional in-person, time-intensive delivery of diabetes education and survival skills training (diabetes survival skills training). Furthermore, diabetes survival skills training has not been designed to meet the specific learning needs of patients with diabetes and their caregivers, particularly if delivered by telehealth.

Improving Prehospital Stroke Diagnosis Using Natural Language Processing of Paramedic Reports.

Background And Purpose: Accurate prehospital diagnosis of stroke by emergency medical services (EMS) can increase treatments rates, mitigate disability, and reduce stroke deaths. We aimed to develop a model that utilizes natural language processing of EMS reports and machine learning to improve prehospital stroke identification.

Methods: We conducted a retrospective study of patients transported by the Chicago EMS to 17 regional primary and comprehensive stroke centers.

Data from fitting Gaussian process models to various data sets using eight Gaussian process software packages.

This data article provides the summary data from tests comparing various Gaussian process software packages. Each spreadsheet represents a single function or type of function using a particular input sample size. In each spreadsheet, a row gives the results for a particular replication using a single package.