A proof-of-concept study to construct Bayesian network decision models for supporting the categorization of sudden unexpected infant death.

ABSTRACT: Sudden infant death syndrome (SIDS) remains a leading cause of infant death in high-income countries. Supporting models for categorization of sudden unexpected infant death into SIDS/non-SIDS could reduce mortality. Therefore, we aimed to develop such a tool utilizing forensic data, but the reduced number of SIDS cases renders this task inherently difficult. To overcome this, we constructed Bayesian network models according to diagnoses performed by expert pathologists and created conditional probability tables in a proof-of-concept study. In the diagnostic support model, the data of 64 sudden unexpected infant death cases was employed as the training dataset, and 16 known-risk factors, including age at death and co-sleeping, were added. In the validation study, which included 8 new cases,

SUBMITTER: Hamayasu H 

PROVIDER: S-EPMC9192651 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature