The Unseen Hand: AI-Based Prescribing Decision Support Tools and the Evaluation of Drug Safety and Effectiveness.

The use of artificial intelligence (AI)-based tools to guide prescribing decisions is full of promise and may enhance patient outcomes. These tools can perform actions such as choosing the 'safest' medication, choosing between competing medications, promoting de-prescribing or even predicting non-adherence. These tools can exist in a variety of formats; for example, they may be directly integrated into electronic medical records or they may exist in a stand-alone website accessible by a web browser.

A new approach to identifying patients with elevated risk for Fabry disease using a machine learning algorithm.

Background: Fabry disease (FD) is a rare genetic disorder characterized by glycosphingolipid accumulation and progressive damage across multiple organ systems. Due to its heterogeneous presentation, the condition is likely significantly underdiagnosed. Several approaches, including provider education efforts and newborn screening, have attempted to address underdiagnosis of FD across the age spectrum, with limited success.

A Bayesian Belief Network Model Assessing the Risk to Wastewater Workers of Contracting Ebola Virus Disease During an Outbreak.

During an outbreak of Ebola virus disease (EVD), hospitals' connections to municipal wastewater systems may provide a path for patient waste bearing infectious viral particles to pass from the hospital into the wastewater treatment system, potentially posing risks to sewer and wastewater workers. To quantify these risks, we developed a Bayesian belief network model incorporating data on virus behavior and survival along with structural characteristics of hospitals and wastewater treatment systems. We applied the model to assess risks under several different scenarios of workers' exposure to wastewater for a wastewater system typical of a mid-sized U.

Advancing Dose-Response Assessment Methods for Environmental Regulatory Impact Analysis: A Bayesian Belief Network Approach Applied to Inorganic Arsenic.

Dose-response functions used in regulatory risk assessment are based on studies of whole organisms and fail to incorporate genetic and metabolomic data. Bayesian belief networks (BBNs) could provide a powerful framework for incorporating such data, but no prior research has examined this possibility. To address this gap, we develop a BBN-based model predicting birthweight at gestational age from arsenic exposure via drinking water and maternal metabolic indicators using a cohort of 200 pregnant women from an arsenic-endemic region of Mexico.