Developing a deep learning natural language processing algorithm for automated reporting of adverse drug reactions.

The detection of adverse drug reactions (ADRs) is critical to our understanding of the safety and risk-benefit profile of medications. With an incidence that has not changed over the last 30 years, ADRs are a significant source of patient morbidity, responsible for 5%-10% of acute care hospital admissions worldwide. Spontaneous reporting of ADRs has long been the standard method of reporting, however this approach is known to have high rates of under-reporting, a problem that limits pharmacovigilance efforts.

Cure of Limb-Threatening XDR Infection: Combining Genome Sequencing, Therapeutic Drug Level Monitoring, and Surgical Debridement.

We describe a case of limb-threatening osteomyelitis and metalware infection with carbapenemase-producing extensively drug-resistant successfully cured with aggressive surgical debridement and combined intravenous fosfomycin and colistin. Real-time therapeutic drug monitoring was used to maximize probability of efficacy and minimize potential for toxicity.

Correction to: A Machine-Learning Algorithm to Optimise Automated Adverse Drug Reaction Detection from Clinical Coding.

CK: Principal Investigator. CM and CK were responsible for the study design and conception; all authors were responsible for acquisition and validation of the data; CM was responsible for analysis and interpretation of the data; and all authors contributed to reviewing drafts of the manuscript and approved the final version for publication.