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Reducing the Impact of Asthma through Data-Driven Insights

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Data and machine learning models are being used by researchers at New Zealand’s Auckland University of Technology (AUT) to mitigate the asthma disease’s effects. Nearly 600,000 people in modern-day New Zealand require asthma medication. Over 120 people in New Zealand died from asthma last year, per the latest recent death statistics from the New Zealand Health Information Service. Asthma is also among the leading reasons kids end up in the hospital.

An NZD1 billion (USD 613m) yearly expenditures loss in total is anticipated, including NZD800 million (USD 490.5) expenditures caused by workday losses, disability-affected life years, and mortality. According to the World Health Organisation, asthma affects over 262 million people worldwide and will be responsible for over 455,000 fatalities this year.

Understanding which people are at the most significant risk and focusing on preventative health care is crucial in reducing the severity of poor asthma outcomes, such as hospitalisations and mortality rates. But how do you know who will be the most vulnerable?

Researchers at AUT’s Data Science Research Centre are using data and machine learning models to uncover the factors at play. For example, one study predicts the daily number of ED visits and admissions due to asthma using meteorological characteristics and air quality predictors as input data streams from the Auckland Council and NIWA.

Darsha Widana, pursuing a doctorate in computer and information sciences, is working on a separate project that uses several data sources to forecast a patient’s unique asthma risk control score. Individuals and medical professionals will benefit from the data-driven insights this provides as they make management and treatment decisions.

Associate Professor Farhaan Mirza, who heads the Data Science Research Centre, predicted that data science will quickly rise to become one of the most intensely explored disciplines in computer science due to its vast potential applications. Data science allows us to better utilise the information at hand to better predict the possibility of future occurrences and outcomes.

Throughout the world, medical care is getting better because of digital technology. The Surgical AI System (SAIS) was co-created by scientists at Caltech and urologists at Keck Medicine of USC in the United States. During this time in their education, surgical residents typically benefit from the counsel of more seasoned physicians. However, a new SAIS system powered by AI may soon change that.

SAIS is studying surgical video footage to establish what procedures are being carried out and how effectively they are being carried out. The goal is to help surgeons better serve their patients by providing information about their strengths and areas for improvement.

With a budget of AU$ 259 million, ‘Acacia’ is the largest project ever undertaken in the Northern Territory, and the government of Australia’s Northern Territory is ensuring that the state’s hospitals have implemented the system. With the new Acacia system, a nurse in an NT Health clinic in a remote Aboriginal village or a doctor in a critical care unit in Darwin will have instantaneous access to the same patient record and the same information about previous care.

Meanwhile, Singapore has begun testing mHealth software to provide mobile users with interactive nursing care. Data shows that this programme helped the elderly keep tabs on their physical and mental health issues successfully. When the app detects a problem with a patient’s vitals, like their blood pressure or glucose level, it can send a message to a nurse. To assess the senior’s health, the nurse will get in touch with them.


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