Precise and Timely Healthcare

Bringing it All Together

For this research theme, we utilise disparate data sources, analyses, and technologies to enable more precise and timely healthcare by way of the following:

  • Develop methods for combining community-sourced data to quantify patient health outside of a clinical environment.
  • Determine how data can enable more accurate and timely care, by providing contextual information to healthcare providers.

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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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 Search: Clinical Document Semantic Search

Smart Search: Clinical Document Semantic Search

A ‘Google’ for Electronic Health Records  Ten minutes can be an eternity for medical professionals that need to make split-second decisions to save lives. If they miss a single piece of vital information it could prove critical – but equally, ...
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Spatio-Temporal Big Data Analysis of Adherence Behaviour in Chronic Disease

Spatio-Temporal Big Data Analysis of Adherence Behaviour in Chronic Disease

Many patients in New Zealand don’t receive the benefits from their medications due to poor medication behaviour, referred to as medication adherence. Previously, data related to medication adherence has been gathered using questionnaire surveys based on the Health Belief Model ...
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Vital Sign Monitoring and Decision Support System

Vital Sign Monitoring and Decision Support System

In its first phase, this research developed a real-time mobile-based vital signs monitoring application in the hospital context using medical devices and clinical decision support techniques. The application, VitalsAssist, was developed with cloud-based security and data storage. Data analysis was ...
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