Application of a Bayesian network to aid the interpretation of blood alcohol (ethanol) concentrations in air crashes. (March 2020)

• Record Type: Journal Article
• Authors: Maskell, Peter D; Jackson, Graham
• Is Part Of: Forensic science international. Volume 308(2020)
• Publication Date: 2020-03
• Subjects: Bayesian network -- Ethanol interpretation -- Air crashes -- Postmortem; Medical jurisprudence -- Periodicals; Chemistry, Forensic -- Periodicals; Forensic Medicine -- Periodicals; Médecine légale -- Périodiques; Chimie légale -- Périodiques; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; Chemistry, Forensic; Medical jurisprudence; Electronic journals; Periodicals; Electronic journals; 614.1
• DOI: 10.1016/j.forsciint.2020.110174
• Languages: English
• ISSNs: 0379-0738

Abstract

Highlights: Important to determine if pilots in air crashes were potentially intoxicated. Bayesian networks allow a rational coherent approach to forensic interpretation. The model presented allows a more objective approach to case interpretation. The requirement for further data to strengthen the Network is highlighted. Abstract: In the investigation of a fatal air crash, it is important to determine if the pilot, at the time of death, was contravening the regulations in relation to 1) the permitted concentration of ethanol (alcohol) in the blood and 2) whether the pilot had consumed alcohol within a specified period before flying. It is also important to assess whether any alcohol detected in the toxicological samples was present either because of consumption or because of post-mortem alcohol formation. We have developed a Bayesian Network that models the relationships between analytical results, circumstantial evidence and the concentration of alcohol at the time of death in cases of air crash. The model provides a rational, coherent approach to forensic interpretation, moving interpretation from a largely subjective, generalist approach to a more objective, case-specific methodology utilising available relevant data and accommodating the inevitable uncertainties within a case.