Tag: machine learning

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 …

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, if they waste precious time searching for data, that too can be damaging to the …

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 for ‘secondary applications’, such as disease diagnosis and readmission prediction, by applying machine learning techniques (in particular, deep neural networks). The difficulty with this approach is that data from EHRs is often inconsistent, episodic, exists …