Below we showcase several projects in which SIH has used inferential modelling. See all projects.
GemPy MCMC Sampling
GemPy is a Python-based, open-source library for implicitly generating 3D structural geological models. It was designed from the ground up to support easy embedding in probabilistic frameworks for the uncertainty analysis of subsurface structures.
SIH investigated the code’s viability for the implementation of various additional sensor models as well the implementation of a data science-friendly Bayesian inference wrapper for easy experimentation with priors, likelihoods and sampling schemes.
Bayesian Updating for Childhood Obesity Grant Proposal
SIH supported a grant proposal by the Centre for Translational Data Science, by demonstrating the value of using Bayesian modelling when collecting and analysing longitudinal data on childhood obesity. We built cross-sectional Bayesian variable selection models to select important factors and models for predicting children’s BMI, mental health and sleep quality across multiple ages, for each child in the Longitudinal Study of Australian Children (LSAC) study. A vector-autoregressive model was then applied to visualise the unexplained variation in the preceding models. We constructed visualisations to demonstrate the importance of understanding uncertainty over the course of data collection, and the potential for using Bayesian adaptive trials during collection.
Discharge Against Medical Advice in Culturally and Linguistically Diverse Patients
In this study we examined discharge against medical advice (DAMA) and its relation to the cultural and linguistic diversity (CALD) of 600,000 patients over 9 years in the Sydney Children’s Hospital Network. Using a bayesian logistic regression framework, we found CALD status to be significantly positively correlated with DAMA rates. Identification of this link provides opportunities for intervention at a practice and policy level in order to prevent adverse outcomes for CALD patients.
1000-fold speedup in Dynamic Bayesian network model
A Bayesian network is a series of linear models fit to describe the relationships between different variables in a time series. If there are change points in how these variables are related, then the network is dynamic.
SIH helped the researcher by speeding up the R-package used to fit the dynamic bayesian network model by 1000x. The R-package is now available at https://github.com/FrankD/EDISON/tree/MultipleTimeSeries
Where can deep-sea iron nodules be found?
Potato-sized nodules of iron ore found on the ocean floor are of commercial mining interest. However, negative ecological effects from mining these nodules is of concern. SIH constructed a global predictive model of nodule occurrence by combining data from thousands of ocean floor samples with global maps of oceanic variables. The environments in which these deposits do and do not occur could then be characterised to generate insight into potential consequences of proposed mining.
Predicting unnecessary CT scans
- Professor Jonathan Morris, Kolling Institute of Medical Research and Sydney Medical School; Dr Felicity Gallimore
- The University of Sydney Medical School
- Data Science (Dr Aldo Saavedra , Dr Madhura Killedar, Dr Joel Nothman and Mr Peter Thiem)
- Predictive modelling Inferential modelling Description and basic visualization Language as data
Diagnostic imaging in hospitals is costly due to expensive machines and their operators, as well as the cost of moving patients in and out of radiography. Published studies of emergency presentations have shown that the number of brain computer tomography (CT-Brain) scans performed is increasing with time while the proportion of scans giving no cause for concern remains the same and represents the largest category.
We sought to determine whether a substantial portion of CT Scans performed in North Sydney LHD were unnecessary. We translated this research question into something determinable from data: identify CT-Brain cases where the unconcerning outcome of scans could be predicted from clinical knowledge available prior to the scan. By first constructing a text classifier to label CT Scan reports as unconcerning, we were able to use clustering and predictive modelling to weakly identify some patient features that predicted unconcerning CT results.
While the project had the potential to impact clinical policy surrounding the application of CT Scans in Emergency Departments, the weak results suggests that if any excessive expenditure problem exists it is not simple to resolve. At the same time, we have developed methodologies for performing similar studies towards rationalising diagnostic scan expenditure.
Predicting Crime using a Spatial-Demographic Framework
Responding to domestic violence related assaults dominate much of the NSW Police’s resources. We try to understand the relationships that drive social-demographic change and cause the occurrence of crime using a complex modelling framework. The social-demographic-crime network and its inter-dependencies were modelled using a Bayesian vector autoregression model. We built a collaboration with BOCSAR, the crime database of all offences in NSW over the last 20 years, and sourced demographic data for multiple census years. The results of this study will help inform policy decision-making by government and police.
Identifying Nerve Function Profiles in Motor Neurodegenerative Disorders
Nerve excitability measurements can identify patterns of nerve dysfunction associated with many diseases of the nervous system. The researchers manage a database containing around 20 years’ of peripheral nerve excitability studies. A software package, QTRAC, is used to generate ~35 properties that are analysed in a research context. Additional information is incorporated to help make a diagnosis, such as clinical survey data, and the temperature of the nerve at the time of the test. Importantly, diagnosis of the disorder is not always 100% accurate. SIH used machine learning to predict the likelihood motor neuron disease for a patient given nerve excitability measurements. The model had reasonable ability to rank individual cases in order of increasing MND risk. SIH delivered this model in a software package for future use in research as well as a clinical setting, with the intention of improving the speed and accuracy of MND diagnosis to improve treatment outcomes for patients.
Discharge against medical advice in the Sydney Children's Hospital Network
Patients who discharge against medical advice (DAMA) from hospital carry a significant risk of readmission and have increased rates of morbidity and mortality. Using five years of admissions and diagnosis data, we sought to identify the demographic, clinical and administrative characteristics of DAMA patients in the Sydney Children’s Hospital Network. Using a bayesian logistic regression framework, we found statistically significant predictors of DAMA in a given admission were hospital site, a mental health/behavioural diagnosis, Aboriginality, emergency rather than elective admissions, a gastrointestinal diagnosis and a history of previous DAMA. Identification of these predictors of DAMA provides opportunities for intervention at a practice and policy level in order to prevent adverse outcomes for patients.