Machine learning to predict poor school performance in paediatric survivors of intensive care: a population-based cohort study

Purpose: Whilst survival in paediatric critical care has improved, clinicians lack tools capable of predicting long-term outcomes. We developed a machine learning model to predict poor school outcomes in children surviving intensive care unit (ICU). Methods: Population-based study of children < 16 years requiring ICU admission in Queensland, Australia, between 1997 and 2019. Failure to meet the National Minimum Standard (NMS) in the National Assessment Program-Literacy and Numeracy (NAPLAN) assessment during primary and secondary school was the primary outcome. Routine ICU information was used to train machine learning classifiers. Models were trained, validated and tested using stratified nested cross-validation. Results: 13,957 childhood ICU survivors with 37,200 corresponding NAPLAN tests after a median follow-up duration of 6 years were included. 14.7%, 17%, 15.6% and 16.6% failed to meet NMS in school grades 3, 5, 7 and 9. The model demonstrated an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.8 (standard deviation SD, 0.01), with 51% specificity to reach 85% sensitivity [relative Area Under the Precision Recall Curve (rel-AUPRC) 3.42, SD 0.06]. Socio-economic status, illness severity, and neurological, congenital, and genetic disorders contributed most to the predictions. In children with no comorbidities admitted between 2009 and 2019, the model achieved a AUROC of 0.77 (SD 0.03) and a rel-AUPRC of 3.31 (SD 0.42). Conclusion: A machine learning model using data available at time of ICU discharge predicted failure to meet minimum educational requirements at school age. Implementation of this prediction tool could assist in prioritizing patients for follow-up and targeting of rehabilitative measures. Keywords: Child, Intensive care, Machine learning, Neurodevelopment, Schoo

Impact of parental and healthcare professional concern on the diagnosis of pediatric sepsis: a diagnostic accuracy study

ObjectiveThe Surviving Sepsis Campaign recommends systematic screening for sepsis. Although many sepsis screening tools include parent or healthcare professional concern, there remains a lack of evidence to support this practice. We aimed to test the diagnostic accuracy of parent and healthcare professional concern in relation to illness severity, to diagnose sepsis in children.DesignThis prospective multicenter study measured the level of concern for illness severity as perceived by the parent, treating nurse and doctor using a cross-sectional survey. The primary outcome was sepsis, defined as a pSOFA score &gt;0. The unadjusted area under receiver-operating characteristic curves (AUC) and adjusted Odds Ratios (aOR) were calculated.SettingTwo specialised pediatric Emergency Departments in QueenslandPatientsChildren aged 30 days to 18 years old that were evaluated for sepsisInterventionNoneMain Results492 children were included in the study, of which 118 (23.9%) had sepsis. Parent concern was not associated with sepsis (AUC 0.53, 95% CI: 0.46–0.61, aOR: 1.18; 0.89–1.58) but was for PICU admission (OR: 1.88, 95% CI: 1.17–3.19) and bacterial infection (aOR: 1.47, 95% CI: 1.14–1.92). Healthcare professional concern was associated with sepsis in both unadjusted and adjusted models (nurses: AUC 0.57, 95% CI-0.50, 0.63, aOR: 1.29, 95% CI: 1.02–1.63; doctors: AUC 0.63, 95% CI: 0.55, 0.70, aOR: 1.61, 95% CI: 1.14–2.19).ConclusionsWhile our study does not support the broad use of parent or healthcare professional concern in isolation as a pediatric sepsis screening tool, measures of concern may be valuable as an adjunct in combination with other clinical data to support sepsis recognition.Clinical Trial RegistrationACTRN12620001340921

Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking

This work presents the current state-of-the-art in techniques for tracking a number of objects moving in a coordinated and interacting fashion. Groups are structured objects characterized with particular motion patterns. The group can be comprised of a small number of interacting objects (e.g. pedestrians, sport players, convoy of cars) or of hundreds or thousands of components such as crowds of people. The group object tracking is closely linked with extended object tracking but at the same time has particular features which differentiate it from extended objects. Extended objects, such as in maritime surveillance, are characterized by their kinematic states and their size or volume. Both group and extended objects give rise to a varying number of measurements and require trajectory maintenance. An emphasis is given here to sequential Monte Carlo (SMC) methods and their variants. Methods for small groups and for large groups are presented, including Markov Chain Monte Carlo (MCMC) methods, the random matrices approach and Random Finite Set Statistics methods. Efficient real-time implementations are discussed which are able to deal with the high dimensionality and provide high accuracy. Future trends and avenues are traced. © 2013 Elsevier Inc. All rights reserved

From imagination to innovation: The effect of pretend play on children's creativity

The present study investigated whether engaging in pretend play, a fundamental part of a child's development, would enhance children's creativity and innovation. Both pretend play and creativity involve the ability to combine ideas in novel ways and apply these ideas to the immediate environment. Thus pretend play may provide children with the opportunity to practice and develop the skills required for creative thought and behaviour. To test this hypothesis, thirty-seven pre-school aged girls were randomly assigned into a pretend play, functional play or control condition. Depending on their condition, children engaged in a 10 minute pretend play sequence, a functional play sequence, or no play before completing five creativity tasks. It was predicted that children in the pretend play condition would provide more novel and imaginative responses to the tasks than those in the functional play or control conditions. The results indicated that there were no significant differences in children's responses between the conditions for each of the five tasks. There was also a large amount of variability in children‟s responses for each task across all conditions. Furthermore, the tasks used to measure creativity were revealed to be unrelated. These results suggest that although creativity and innovation are highly valued, measuring these qualities can be challenging, due to the variability associated with creative performance. The implications of these findings for future research are discussed

The zeros of the derivatives of a real entire function of finite order

SIGLEAvailable from British Library Document Supply Centre- DSC:DX183176 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Methods for personalised predictive modelling of developmental milestones for children with disabilities

This thesis developed methods for personalised modelling of developmental milestones for children with disabilities. Using data containing 348 milestone measurements from a small sample of children with a diverse range of disabilities, methods were developed to create a comprehensive personalised developmental profile for each child. These profiles incorporate multiple developmental domains and are designed to be updated in real time so that parents can be provided with feedback as their child develops. The outputs of the methods developed in this thesis will be used to help inform decision-making and assist with personalised intervention planning at the Developing Foundation

Bayesian Hierarchical Multidimensional Item Response Modeling of Small Sample, Sparse Data for Personalized Developmental Surveillance

Developmental surveillance tools are used to closely monitor the early development of infants and young children. This study provides a novel implementation of a multidimensional item response model, using Bayesian hierarchical priors, to construct developmental profiles for a small sample of children (N = 115) with sparse data collected through an online developmental surveillance tool. The surveillance tool records 348 developmental milestones measured from birth to three years of age, within six functional domains: auditory, hands, movement, speech, tactile, and vision. The profiles were constructed in three steps: (1) the multidimensional item response model, embedded in the Bayesian hierarchical framework, was implemented in order to measure both the latent abilities of the children and attributes of the milestones, while retaining the correlation structure among the latent developmental domains; (2) subsequent hierarchical clustering of the multidimensional ability estimates enabled identification of subgroups of children; and (3) information from the posterior distributions of the item response model parameters and the results of the clustering were used to construct a personalized profile of development for each child. These individual profiles support early identification of, and personalized early interventions for, children with developmental delay.</p