Machine Learning Approaches to Determine Feature Importance for Predicting Infant Autopsy Outcome

Introduction: Sudden unexpected death in infancy (SUDI) represents the commonest presentation of postneonatal death. We explored whether machine learning could be used to derive data driven insights for prediction of infant autopsy outcome. Methods: A paediatric autopsy database containing >7,000 cases, with >300 variables, was analysed by examination stage and autopsy outcome classified as ‘explained (medical cause of death identified)’ or ‘unexplained’. Decision tree, random forest, and gradient boosting models were iteratively trained and evaluated. Results: Data from 3,100 infant and young child (<2 years) autopsies were included. Naïve decision tree using external examination data had performance of 68% for predicting an explained death. Core data items were identified using model feature importance. The most effective model was XG Boost, with overall predictive performance of 80%, demonstrating age at death, and cardiovascular and respiratory histological findings as the most important variables associated with determining medical cause of death. Conclusion: This study demonstrates feasibility of using machine-learning to evaluate component importance of complex medical procedures (paediatric autopsy) and highlights value of collecting routine clinical data according to defined standards. This approach can be applied to a range of clinical and operational healthcare scenario

Impact of Scottish vocational qualifications on residential child care : have they fulfilled the promise?

This article will present findings from a doctoral study exploring the impact of 'SVQ Care: Promoting Independence (level III)' within children's homes. The study focuses on the extent to which SVQs enhance practice and their function within a 'learning society'. A total of 30 staff were selected from seven children's homes in two different local authority social work departments in Scotland. Each member of staff was interviewed on four separate occasions over a period of 9 months. Interviews were structured using a combination of repertory grids and questions. Particular focus was given to the assessment process, the extent to which SVQs enhance practice and the learning experiences of staff. The findings suggest that there are considerable deficiencies both in terms of the SVQ format and the way in which children's homes are structured for the assessment of competence. Rather than address the history of failure within residential care, it appears that SVQs have enabled the status quo to be maintained whilst creating an 'illusion' of change within a learning society

Structural analysis of the Ss sialoglycoprotein specific for Henshaw blood group from human erythrocyte membranes

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66122/1/j.1432-1033.1984.tb08155.x.pd

Population Genetics of GYPB and Association Study between GYPB*S/s Polymorphism and Susceptibility to P. falciparum Infection in the Brazilian Amazon

Merozoites of Plasmodium falciparum invade through several pathways using different RBC receptors. Field isolates appear to use a greater variability of these receptors than laboratory isolates. Brazilian field isolates were shown to mostly utilize glycophorin A-independent invasion pathways via glycophorin B (GPB) and/or other receptors. The Brazilian population exhibits extensive polymorphism in blood group antigens, however, no studies have been done to relate the prevalence of the antigens that function as receptors for P. falciparum and the ability of the parasite to invade. Our study aimed to establish whether variation in the GYPB*S/s alleles influences susceptibility to infection with P. falciparum in the admixed population of Brazil.Two groups of Brazilian Amazonians from Porto Velho were studied: P. falciparum infected individuals (cases); and uninfected individuals who were born and/or have lived in the same endemic region for over ten years, were exposed to infection but have not had malaria over the study period (controls). The GPB Ss phenotype and GYPB*S/s alleles were determined by standard methods. Sixty two Ancestry Informative Markers were genotyped on each individual to estimate admixture and control its potential effect on the association between frequency of GYPB*S and malaria infection.GYPB*S is associated with host susceptibility to infection with P. falciparum; GYPB*S/GYPB*S and GYPB*S/GYPB*s were significantly more prevalent in the in the P. falciparum infected individuals than in the controls (69.87% vs. 49.75%; P<0.02). Moreover, population genetics tests applied on the GYPB exon sequencing data suggest that natural selection shaped the observed pattern of nucleotide diversity.Epidemiological and evolutionary approaches suggest an important role for the GPB receptor in RBC invasion by P. falciparum in Brazilian Amazons. Moreover, an increased susceptibility to infection by this parasite is associated with the GPB S+ variant in this population

Machine Learning Approaches to Determine Feature Importance for Predicting Infant Autopsy Outcome

Introduction: Sudden unexpected death in infancy (SUDI) represents the commonest presentation of postneonatal death. We explored whether machine learning could be used to derive data driven insights for prediction of infant autopsy outcome. Methods: A paediatric autopsy database containing >7,000 cases, with >300 variables, was analysed by examination stage and autopsy outcome classified as ‘explained (medical cause of death identified)’ or ‘unexplained’. Decision tree, random forest, and gradient boosting models were iteratively trained and evaluated. Results: Data from 3,100 infant and young child (<2 years) autopsies were included. Naïve decision tree using external examination data had performance of 68% for predicting an explained death. Core data items were identified using model feature importance. The most effective model was XG Boost, with overall predictive performance of 80%, demonstrating age at death, and cardiovascular and respiratory histological findings as the most important variables associated with determining medical cause of death. Conclusion: This study demonstrates feasibility of using machine-learning to evaluate component importance of complex medical procedures (paediatric autopsy) and highlights value of collecting routine clinical data according to defined standards. This approach can be applied to a range of clinical and operational healthcare scenario