Tag: machine learning

Precision Driven Health CEO selected to present on ethical machine learning at HIMSS20

Precision Driven Health (PDH) has today announced that its Chief Executive Officer, Dr Kevin Ross, has been selected to deliver a presentation on ethical machine learning at the 2020 HIMSS Global Health Conference and Exhibition. Dr Ross has also been invited to be a panellist in the AsiaPac Summit during the global conference. HIMSS20 brings …

Precision Driven Health partners with MoleMap in AI project to improve skin cancer detection

Precision Driven Health (PDH), New Zealand’s leading health data research initiative, has today announced it is working with MoleMap on its use of Artificial Intelligence (AI) to improve the early detection of skin cancer. PDH Chief Executive Dr Kevin Ross says the $2 million research project will enhance the AI algorithm that MoleMap has developed …

Connecting with the health data science community in Spain

By Anna Spyker, Software Engineer Recently I attended the IEEE CBMS Conference in Cordoba, Spain to connect with other researchers, data scientists and clinicians and share the work we’ve been doing on interpretable machine learning. I felt an incredible sense of community at the conference. As it was a smaller conference, there was a lot …

Deep learning-based Melanoma prediction from skin images

Summer of Research project by Sivaram Manoharan, supervised by Bernhard Pfahringer, University of Auckland. Melanoma is an extremely dangerous type of skin cancer most commonly caused by exposure to UV light. The highest incidence rate of Melanoma is in Australia and New Zealand, in addition to being the fourth most common cancer diagnosed in New …

Machine readable clinical guidelines and pathways

Summer of Research project by David Bassett, supervised by Dr. Patrick Gladding (University of Auckland) There has recently been rapid growth in the use of clinical guidelines and pathways for the assessment, diagnosis and management of medical conditions. These guidelines generally contain unstructured free text and are difficult to digitise into electronic health records, limiting …

Application of deep learning techniques in a de-identification system

Summer of Research project by Yicheng Shi, University of Auckland, supervised by Quentin Thurier (Orion Health). Clinical data, collected by healthcare providers when treating patients, is incredibly valuable for medical research – but good data is hard to get. This information can help researchers improve healthcare. For example, by studying a patient’s medical history, researchers …

Interpretable image-based machine learning models in healthcare

Summer of Research project by Harper Shen, University of Auckland, supervised by Quentin Thurier (Orion Health) and Dr Yun Sing Koh (University of Auckland). Neural networks can be great at solving problems, but they sometimes give wrong answers. Would you trust an algorithm that got life-saving information right 80% of the time? It is easier …

Deep Learning for Triaging GP Referrals

The patient journey is one that we have all experienced. A complex and unique journey for each patient, it usually begins at our local medical centre. Manned by the gatekeepers of healthcare, our GPs, this crucial stage of the journey is usually the one that determines whether we enter secondary care. From here, a series …

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 coronary heart disease) and stroke but have lower access to health care. It is …

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 actively trying to remedy this situation which is especially problematic in healthcare, largely because legal accountability and ethics have greater emphasis and importance in the healthcare decision-making process than in …