The High-resolution Urban Meteorology for Impacts Dataset (HUMID) daily for the Conterminous United States.

Many current gridded surface meteorological datasets are inadequate for quantifying near-surface spatiotemporal variability because they do not fully represent the impacts of land surface heterogeneity. Of note, explicit representation of the spatial structure and magnitude of local urban warming are usually lacking. Here we enhance the representation of spatial meteorological variability over urban areas in the conterminous United States (CONUS) by employing the High-Resolution Land Data Assimilation System (HRLDAS), which accounts for the fine-scale impacts of spatiotemporally varying land surfaces on weather.

Quality Improvement to Identify and Address Food Insecurity During Pediatric Hospitalizations.

Objectives: Hospitalized children represent a vulnerable population with high rates of unidentified food insecurity (FI). We aimed to improve FI screening for eligible families from 0% to 60%. Secondarily, we sought to provide location-based food resources to families that screened positive.

MedT5SQL: a transformers-based large language model for text-to-SQL conversion in the healthcare domain.

Introduction: In response to the increasing prevalence of electronic medical records (EMRs) stored in databases, healthcare staff are encountering difficulties retrieving these records due to their limited technical expertise in database operations. As these records are crucial for delivering appropriate medical care, there is a need for an accessible method for healthcare staff to access EMRs.

Methods: To address this, natural language processing (NLP) for Text-to-SQL has emerged as a solution, enabling non-technical users to generate SQL queries using natural language text.