Development and Validation of an Interpretable Machine Learning Model for Early Prognosis Prediction in ICU Patients with Malignant Tumors and Hyperkalemia.

This study aims to develop and validate a machine learning (ML) predictive model for assessing mortality in patients with malignant tumors and hyperkalemia (MTH). We extracted data on patients with MTH from the Medical Information Mart for Intensive Care-IV, version 2.2 (MIMIC-IV v2.

Experimental study on the kinetic effect of -butyl--methylpyrrolidinium tetrafluoroborate and poly(-vinyl-caprolactam) on CH hydrate formation.

In this work, a series of experiments were carried out to study the kinetic inhibition performance of -butyl--methylpyrrolidinium tetrafluoroborate ([BMP][BF]), poly(-vinylcaprolactam) (PVCap) and compound inhibitor systems on methane hydrate from both macroscopic and microscopic perspectives. In the macroscopic experiments, the influence of the concentration, the ratio of inhibitors, the subcooling on the induction time and gas consumption rate of methane hydrate were studied. The results indicated that [BMP][BF] could inhibit the growth rate of CH hydrate, but failed to delay the nucleation.