Introduction Artificial intelligence based on machine learning has made large advancements in many fields of science and medicine but its impact on pharmacovigilance is yet unclear. Objective The present study conducted a scoping review of the use of artificial intelligence based on machine learning to understand how it is used for pharmacovigilance tasks, characterize differences with other fields, and identify opportunities to improve pharmacovigilance through the use of machine learning. Design The PubMed, Embase, Web of Science, and IEEE Xplore databases were searched to identify articles pertaining to the use of machine learning in pharmacovigilance published from the year 2000 to September 2021. After manual screening of 7744 abstracts, a total of 393 papers met the inclusion criteria for further analysis. Extraction of key data on study design, data sources, sample size, and machine learning methodology was performed. Studies with the characteristics of good machine learning practice were defined and manual review focused on identifying studies that fulfilled these criteria and results that showed promise. Results The majority of studies (53%) were focused on detecting safety signals using traditional statistical methods. Of the studies that used more recent machine learning methods, 61% used off-the-shelf techniques with minor modifications. Temporal analysis revealed that newer methods such as deep learning have shown increased use in recent years. We found only 42 studies (10%) that reflect current best practices and trends in machine learning. In the subset of 154 papers that focused on data intake and ingestion, 30 (19%) were found to incorporate the same best practices. Conclusion Advances from artificial intelligence have yet to fully penetrate pharmacovigilance, although recent studies show signs that this may be changing. Supplementary Information The online version contains supplementary material available at 10.1007/s40264-022-01176-1.

Traumatic brain injury (TBI) is a leading neurological cause of death and disability across the world. Early characterization of TBI severity could provide a window for therapeutic intervention and contribute to improved outcome. We hypothesized that granular electronic health record data available in the first 24 h following admission to the intensive care unit (ICU) can be used to differentiate outcomes at discharge. Working from two ICU datasets we focused on patients with a primary admission diagnosis of TBI whose length of stay in ICU was ≥ 24 h (N = 1689 and 127). Features derived from clinical, laboratory, medication, and physiological time series data in the first 24 h after ICU admission were used to train elastic-net regularized Generalized Linear Models for the prediction of mortality and neurological function at ICU discharge. Model discrimination, determined by area under the receiver operating characteristic curve (AUC) analysis, was 0.903 and 0.874 for mortality and neurological function, respectively. Model performance was successfully validated in an external dataset (AUC 0.958 and 0.878 for mortality and neurological function, respectively). These results demonstrate that computational analysis of data routinely collected in the first 24 h after admission accurately and reliably predict discharge outcomes in ICU stratum TBI patients.

Transarterial embolization (TAE) is a standard-of-care treatment for tumors in which embolic particles are locally injected via a catheter to occlude blood flow and induce ischemia in the target tissue. Physicians currently rely on subjective visual cues from fluoroscopy in order to determine the procedural endpoint relative to the injection site. This contributes to highly variable treatment outcomes, including the accumulation of embolic particles in healthy tissue, called off-target embolization. To address this concern, we describe a novel, multi-lumen catheter that 1) measures real-time pressure upstream of the tumor site during TAE injection; and 2) associates that measurement with the volume of embolic particles injected. Using an in vitro silicon vascular model, we characterize the relationship between blood flow, intravascular pressure, and injection pressure. Furthermore, we identify a predictive pressure curve based on the volume of embolic particles injected. This approach has the potential to standardize and optimize TAE, reducing the likelihood of incomplete or off-target embolization, and improving patient outcomes.

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