Accuracy of symptom checker for the diagnosis of sexually transmitted infections using machine learning and Bayesian network algorithms.

Background: A significant proportion of individuals with symptoms of sexually transmitted infection (STI) delay or avoid seeking healthcare, and digital diagnostic tools may prompt them to seek healthcare earlier. Unfortunately, none of the currently available tools fully mimic clinical assessment or cover a wide range of STIs.

Methods: We prospectively invited attendees presenting with STI-related symptoms at Melbourne Sexual Health Centre to answer gender-specific questionnaires covering the symptoms of 12 common STIs using a computer-assisted self-interviewing system between 2015 and 2018.

Modeling COVID-19 disease processes by remote elicitation of causal Bayesian networks from medical experts.

Background: COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. Better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes.

A flexible method for parameterizing ranked nodes in Bayesian networks using Beta distributions.

When novice modelers first attempt to build a Bayesian network, they are often impressed with the intuitive graphical structures that capture their causal understanding. This favorable impression evaporates on proceeding to parameterization. Conditional probability tables (CPT) require parameters for often hundreds of very similar scenarios and specifying them in the absence of data can be overwhelming.

An Integrated Biosecurity Risk Assessment Model (IBRAM) For Evaluating the Risk of Import Pathways for the Establishment of Invasive Species.

An important aspect of analyzing the risk of unwanted organisms establishing in an area is understanding the pathways by which they arrive. Evaluating the risks of these pathways requires use of data from multiple sources, which frequently are uncertain. To address the needs of agencies responsible for biosecurity operations, we present an Integrated Biosecurity Risk Assessment Model (IBRAM) for evaluating the risk of establishment and dispersal of invasive species along trade pathways.

Novel method to select meaningful outcomes for evaluation in clinical trials.

Background: A standardised framework for selecting outcomes for evaluation in trials has been proposed by the Core Outcome Measures in Effectiveness Trials working group. However, this method does not specify how to ensure that the outcomes that are selected are causally related to the disease and the health intervention being studied. Causal network diagrams may help researchers identify outcomes that are both clinically meaningful and likely to be causally dependent on the intervention, and endpoints that are, in turn, causally dependent on those outcomes.

Risk Analysis Frameworks Used in Biological Control and Introduction of a Novel Bayesian Network Tool.

Classical biological control, the introduction of natural enemies to new environments to control unwanted pests or weeds, is, despite numerous successful examples, associated with rising concerns about unwanted environmental impacts such as population decline of nontarget species. Recognition of these biosafety risks is globally increasing, and prerelease assessments of biological control agents (BCAs) have become more rigorous in many countries. We review the current approaches to risk assessment for BCAs as used in Australasia, Europe, and North America.

Bridging the gaps in test interpretation of SARS-CoV-2 through Bayesian network modelling.

In the absence of an established gold standard, an understanding of the testing cycle from individual exposure to test outcome report is required to guide the correct interpretation of severe acute respiratory syndrome-coronavirus-2 reverse transcriptase real-time polymerase chain reaction (RT-PCR) results and optimise the testing processes. Bayesian network models have been used within healthcare to bring clarity to complex problems. We use this modelling approach to construct a comprehensive framework for understanding the real-world predictive value of individual RT-PCR results.

Predicting forest insect flight activity: A Bayesian network approach.

Daily flight activity patterns of forest insects are influenced by temporal and meteorological conditions. Temperature and time of day are frequently cited as key drivers of activity; however, complex interactions between multiple contributing factors have also been proposed. Here, we report individual Bayesian network models to assess the probability of flight activity of three exotic insects, Hylurgus ligniperda, Hylastes ater, and Arhopalus ferus in a managed plantation forest context.

Predicting island biosecurity risk from introduced fauna using Bayesian Belief Networks.

Around the globe, islands are the last refuge for many threatened and endemic species. Islands are frequently also important sites for recreation, cultural activities, and industrial development, all of which facilitate the establishment of invasive species. Surveillance is employed on islands to detect the establishment of invasive species after their arrival, leading to decisions about follow-up actions.