Modeling disagreement in automatic data labeling for semi-supervised learning in Clinical Natural Language Processing.

Introduction: Computational models providing accurate estimates of their uncertainty are crucial for risk management associated with decision-making in healthcare contexts. This is especially true since many state-of-the-art systems are trained using the data which have been labeled automatically (self-supervised mode) and tend to overfit.

Methods: In this study, we investigate the quality of uncertainty estimates from a range of current state-of-the-art predictive models applied to the problem of observation detection in radiology reports.

Spent Lithium-Ion Batteries Derived CoO for Electrocatalytic Polyethylene Terephthalate Plastic Recycling.

Spent lithium-ion batteries (LIBs) are an essential secondary resource containing valuable metal elements. Transforming spent LIBs into efficient catalysts through a simple process presents a promising strategy to address both metal resource scarcity and clean energy challenges. Herein, a deep eutectic solvent-assisted synthesis of CoO material from spent LIBs is proposed.