Comparison of carbon dioxide control during pressure controlled versus pressure-regulated volume controlled ventilation in children (CoCO2): protocol for a pilot digital randomised controlled trial in a quaternary paediatric intensive care unit.

Introduction: Digital trials are a promising strategy to increase the evidence base for common interventions and may convey considerable efficiency benefits in trial conduct. Although paediatric intensive care units (PICUs) are rich in routine electronic data, highly pragmatic digital trials in this field remain scarce. There are unmet evidence needs for optimal mechanical ventilation modes in paediatric intensive care.

Word sense disambiguation of acronyms in clinical narratives.

Clinical narratives commonly use acronyms without explicitly defining their long forms. This makes it difficult to automatically interpret their sense as acronyms tend to be highly ambiguous. Supervised learning approaches to their disambiguation in the clinical domain are hindered by issues associated with patient privacy and manual annotation, which limit the size and diversity of training data.

Simulation and annotation of global acronyms.

Motivation: Global acronyms are used in written text without their formal definitions. This makes it difficult to automatically interpret their sense as acronyms tend to be ambiguous. Supervised machine learning approaches to sense disambiguation require large training datasets.

Text Mining of Adverse Events in Clinical Trials: Deep Learning Approach.

Background: Pharmacovigilance and safety reporting, which involve processes for monitoring the use of medicines in clinical trials, play a critical role in the identification of previously unrecognized adverse events or changes in the patterns of adverse events.

Objective: This study aims to demonstrate the feasibility of automating the coding of adverse events described in the narrative section of the serious adverse event report forms to enable statistical analysis of the aforementioned patterns.

Methods: We used the Unified Medical Language System (UMLS) as the coding scheme, which integrates 217 source vocabularies, thus enabling coding against other relevant terminologies such as the International Classification of Diseases-10th Revision, Medical Dictionary for Regulatory Activities, and Systematized Nomenclature of Medicine).