Clinical Validation of Explainable Deep Learning Model for Predicting the Mortality of In-Hospital Cardiac Arrest Using Diagnosis Codes of Electronic Health Records.

Background: Using deep learning for disease outcome prediction is an approach that has made large advances in recent years. Notwithstanding its excellent performance, clinicians are also interested in learning how input affects prediction. Clinical validation of explainable deep learning models is also as yet unexplored.

Exploiting Machine Learning Technologies to Study the Compound Effects of Serum Creatinine and Electrolytes on the Risk of Acute Kidney Injury in Intensive Care Units.

Assessing the risk of acute kidney injury (AKI) has been a challenging issue for clinicians in intensive care units (ICUs). In recent years, a number of studies have been conducted to investigate the associations between several serum electrolytes and AKI. Nevertheless, the compound effects of serum creatinine, blood urea nitrogen (BUN), and clinically relevant serum electrolytes have yet to be comprehensively investigated.

Electrostatically Complexed Natural Polysaccharides as Aqueous Barrier Coatings for Sustainable and Recyclable Fiber-Based Packaging.

Driven by the ever-growing awareness of sustainability and circular economy, renewable, biodegradable, and recyclable fiber-based packaging materials are emerging as alternatives to fossil-derived, nonbiodegradable single-use plastics for the packaging industry. However, without functional barrier coatings, the water/moisture vulnerability and high permeability of fiber-based packaging significantly restrain its broader application as primary packaging for food, beverages, and drugs. Herein, we develop waterborne complex dispersion barrier coatings consisting of natural, biodegradable polysaccharides (i.