According to a study by the NYU Center for the Study of Asian American Health (CSAAH), diabetes, which increases the risk of heart diseases, is rife among South Asians. The report's findings conclude that in the United States, South Asian immigrants are 7 times more likely to have type 2 diabetes than the general population, and in New York City, Indian immigrants are at a greater risk of hospitalization for diabetes than other immigrants.
IBM this week presented research investigating how AI and machine learning could be used to improve maternal health in developing countries and predict the onset and progression of Type 1 diabetes. In a study funded by the Bill and Melinda Gates Foundation, IBM researchers built models to analyze demographic datasets from African countries, finding "data-supported" links between the number of years between pregnancies and the size of a woman's social network with birth outcomes. In a separate work, a team from IBM analyzed data across three decades and four countries to attempt to anticipate the onset of Type 1 diabetes anywhere from 3 to 12 months before it's typically diagnosed and then predict its progression. They claim one of the models accurately predicted progression 84% of the time. Despite a global decline in child mortality rates, many countries aren't on track to achieving proposed targets of ending preventable deaths among newborns and children under the age of 5. Unsurprisingly, the progress toward these targets remains uneven, reflected in disparities in access to healthcare services and inequitable resource allocation. Toward potential solutions, researchers at IBM attempted to identify features associated with neonatal mortality "as captured in nationally representative cross-sectional data."
The ability to apply artificial intelligence (AI) to ophthalmology is gathering pace, a consequence of remarkable collaboration between eye specialists and technologists whose forte is the ability to process vast amounts of data quickly. Irish ophthalmologist Dr Pearse Keane – based in Moorfields Hospital, London – has been the chief catalyst in developing AI software to detect 50 sight-threatening eye diseases. It operates by interpreting optical coherence tomography (OCT) scans of the back of the eye, which soon will be routine when going for an eye check. Automation in analysing scans for diseases such as wet age-related macular degeneration (AMD), the main cause of blindness in Europe, and diabetic retinopathy, is about to revolutionise patient outcomes with faster results affording earlier diagnosis and prompt treatment, and ultimately preventing avoidable sight loss. Since that initial breakthrough, the Keane team has developed an alert system for a third of people with AMD who later get it in their good eye and, potentially, an early-warning system for onset of neurodegenerative diseases, notably Alzheimer's.
The possibilities opened up to us by the rise of the Internet of Things (IoT) is a beautiful thing. However, not enough attention is being paid to the software that goes into the things of IoT. This can be a daunting challenge, since, unlike centralized IT infrastructure, there are, by one estimate, at least 30 billion IoT devices now in the world, and every second, 127 new IoT devices are connected to the internet. They are increasingly growing sophisticated and intelligent in their own right, housing significant amounts of local code. The catch is that means a lot of software that needs tending.
Machine learning, deep learning, and artificial intelligence are a collection of algorithms used to identify patterns in data. These algorithms have exotic-sounding names like "random forests", "neural networks", and "spectral clustering". In this post, I will show you how to use one of these algorithms called a "support vector machines" (SVM). I don't have space to explain an SVM in detail, but I will provide some references for further reading at the end. I am going to give you a brief introduction and show you how to implement an SVM with Python.
New technology can quickly and accurately monitor glucose levels in people with diabetes without painful finger pricks to draw blood. A palm-sized device developed by researchers at the University of Waterloo uses radar and artificial intelligence (AI) to non-invasively read blood inside the human body. "The key advantage is simply no pricking," said George Shaker, an engineering professor at Waterloo. "That is extremely important for a lot of people, especially elderly people with very sensitive skin and children who require multiple tests throughout the day." About the same size as existing glucometers, the rectangular device works by sending radio waves through the skin and into blood vessels when users place the tip of their finger on a touchpad.
Artificial intelligence is the key to healthcare breakthroughs. AI tools assist rather than replace healthcare professionals by providing shared decision making and improving personalized, patient-centered care. GlucosePATH equips physicians with various treatment options for patients with type 2 diabetes by curating their particular needs and integrating the medication cost into the treatment decision-making process. This project demonstrates how to reach therapeutic goals by integrating GlucosePATH software into your practice. An opportunity to act will be provided at the end of this series.