Below we showcase several projects in which SIH has used data linkage. See all projects.
ProteHome: Proteomics Experimental Results Database
The Metabolic Cybernetics Lab at the Charles Perkins Centre has generated disparate Mass Spectrometry datasets for protein metabolism studies. With datasets stored in various formats and places, it has been difficult to search and compare experimental results.
SIH developed ProteHome, a bioinformatics system providing a centralised data repository, with a web-based interface to facilitate the:
- Standardisation of quantitative analysis results, with common specification of experimental metadata and formatting of analysis data.
- Retrieving those results for protein(s) or modification(s) of interest, regardless of the version of protein identification number used in the stored experiment.
- Management of submitted datasets using a comprehensive hierarchical storage structure.
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.
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.
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.