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        1-12 of 84 results for 'machine learning'

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        Personalised, machine learning based prediction of asthma in children

        Rachel Graham Foong Hall RF GL BSc (hons), PhD, MBiostat BAppSci PhD CRFS FANZSRS FThorSoc FERS Senior Research Fellow Honorary Research Associate 08 6319 1626 Rachel.Foong@telethonkids.org.au Senior Research Fellow Honorary Research Associate Dr ...

        Research

        Does machine learning have a role in the prediction of asthma in children?

        Asthma is the most common chronic lung disease in childhood. There has been a significant worldwide effort to develop tools/methods to identify children's risk for asthma as early as possible for preventative and early management strategies. Unfortunately, most childhood asthma prediction tools using conventional statistical models have modest accuracy, sensitivity, and positive predictive value. ...

        Research

        Machine Learning-Based DNA Methylation Score for Fetal Exposure to Maternal Smoking

        Fetal exposure to maternal smoking during pregnancy is associated with the development of noncommunicable diseases in the offspring. Maternal smoking may induce such long-term effects through persistent changes in the DNA methylome, which therefore hold the potential to be used as a biomarker of this early life exposure. With declining costs for measuring DNA methylation, we aimed to develop a DNA methylation score that can be used on adolescent DNA methylation data and thereby generate a score for in utero cigarette smoke exposure. ...

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        Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification

        We now have a growing number of epigenetic alterations in disease, and this offers a chance to increase sensitivity and specificity of future diagnostics and therapies ...

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        Application of Inertial Measurement Units and Machine Learning Classification in Cerebral Palsy: Randomized Controlled Trial

        Cerebral palsy (CP) is a physical disability that affects movement and posture. Approximately 17 million people worldwide and 34,000 people in Australia are living with CP. In clinical and kinematic research, goniometers and inclinometers are the most commonly used clinical tools to measure joint angles and positions in children with CP. ...

        Research

        Predicting language difficulties in middle childhood from early developmental milestones: A comparison of traditional regression and machine learning techniques

        The current study provides preliminary evidence that machine learning algorithms provide equivalent predictive accuracy to traditional methods for language difficulties in middle childhood ...

        Research

        Paediatric Medicinal Formulation Development: Utilising Human Taste Panels and Incorporating Their Data into Machine Learning Training

        This review paper explores the role of human taste panels and artificial neural networks (ANNs) in taste-masking paediatric drug formulations. Given the ethical, practical, and regulatory challenges of employing children, young adults (18-40) can serve as suitable substitutes due to the similarity in their taste sensitivity. Taste panellists need not be experts in sensory evaluation so long as a reference product is used during evaluation; however, they should be screened for bitterness taste detection thresholds. ...

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        Gene filtering strategies for machine learning guided biomarker discovery using neonatal sepsis RNA-seq data

        Machine learning (ML) algorithms are powerful tools that are increasingly being used for sepsis biomarker discovery in RNA-Seq data. RNA-Seq datasets contain multiple sources and types of noise (operator, technical and non-systematic) that may bias ML classification. Normalisation and independent gene filtering approaches described in RNA-Seq workflows account for some of this variability and are typically only targeted at differential expression analysis rather than ML applications. ...

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        Development of a pharmaceutical science systematic review process using a semi-automated machine learning tool: Intravenous drug compatibility in the neonatal intensive care setting

        Our objective was to establish and test a machine learning-based screening process that would be applicable to systematic reviews in pharmaceutical sciences. We used the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research type) model, a broad search strategy, and a machine learning tool (Research Screener) to identify relevant references related to y-site compatibility of 95 intravenous drugs used in neonatal intensive care settings. ...

        Research

        Development of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015

        Preterm birth is a global public health problem with a significant burden on the individuals affected. The study aimed to extend current research on preterm birth prognostic model development by developing and internally validating models using machine learning classification algorithms and population-based routinely collected data in Western Australia. ...

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        Machine Learning-Based Single Cell and Integrative Analysis Reveals That Baseline mDC Predisposition Correlates With Hepatitis B Vaccine Antibody Response

        Vaccination to prevent infectious disease is one of the most successful public health interventions ever developed. And yet, variability in individual vaccine effectiveness suggests that a better mechanistic understanding of vaccine-induced immune responses could improve vaccine design and efficacy. ...

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        Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

        Almost half of stillbirths could be potentially identified antenatally based on a combination of factors ...

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