Predictive Analytics

Big Data in Healthcare

In this research we utilise a variety of big data sources for predictive modelling in a healthcare setting. Our research aims to:

  • Develop a framework for classifying patients.
  • Develop predictive models to identify high-risk patients.
  • Develop predictive models for describing patient pathways through the healthcare system.
  • Research methods of producing information to help healthcare managers control costs.

View our projects in this theme below.

In Progress

A Deep Learning Platform for GP Referral Triage

A Deep Learning Platform for GP Referral Triage

Countering bias in the health system Heart disease is the leading cause of preventable mortality in Aotearoa, New Zealand. It is a disease which disproportionately affects certain groups such as Māori – who have higher rates of ischaemic heart disease (or ...
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Clinical Risk Assessments and Calculators

Clinical Risk Assessments and Calculators

Recent studies have indicated that the current risk calculators are out of date and not specific to local contexts and population. This project is testing the hypothesis: Can we build risk calculators that are more accurate for the local population ...
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Feasibility of Analysis of Lab Result Patterns for Patient Results

Feasibility of Analysis of Lab Result Patterns for Patient Results

This feasibility study, in partnership with Vensa Health, is answering the following questions: How can we accurately automate interpretation and dissemination of lab results to save clinician time, while keeping the clinical control in their hands in way that’s intuitive ...
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Interpretable Machine Learning

Interpretable Machine Learning

The “black box” metaphor is commonly used to refer to the lack of understanding of how modern Machine Learning (ML) systems make decisions. Researchers are working actively to remedy this situation which is especially problematic in Healthcare where legal accountability ...
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Smart Medication Reconciliation

Smart Medication Reconciliation

Automating for accuracy Patients’ regular medicines are often inaccurately prescribed when they transfer from hospital to home; a major source of medication errors and iatrogenic harm. Medicines reconciliation (MedRec) is an evidence-based process where clinicians manually compare a patient’s regular ...
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surgical outcome

Surgical Outcome Calculator

Present methods for describing surgical outcomes greatly underestimate the risks to patients. The aim of this project is to find a method that describes post-operative risk more accurately and then develop a surgical risk calculator. The research is wide-reaching and ...
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Completed

Health Outcome Prediction Engine

Health Outcome Prediction Engine

Health Outcome Prediction Engine (HOPE), comprising HOPE for Stroke HOPE for AAA (Abdominal Aortic Aneurysm) HOPE for Patient Reported Outcome ...
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Deep Representation Learning from Electronic Health Records

Deep Representation Learning from Electronic Health Records

As Electronic Health Records (EHRs) become more ubiquitous, healthcare providers are beginning to appreciate the benefits of using this data ...
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