This paper focuses on text summarization in the context of medical dialogue. The idea is that when a patient has a conversation with a doctor, you want to be able to automatically summarize what transpired in the conversation. Doctor: "What have you been experiencing today?" Doctor: "How severe is the pain on a scale from 1 to 10" Then you want to be able to produce a transcript-like record of what happened during the visit. In this paper, the authors propose a model that can summarize these dialogues by filling in words using local structures.
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.
Mohamed Taha's ambition to disrupt the fertility industry began after his first sperm test. He had just been diagnosed with a kidney disease (which later turned out to be a misdiagnosis) and his doctor advised him to freeze his sperm as a precaution. According to the World Health Organisation, a normal sperm count is around 15 million sperm per millilitre (m/ml) of semen. Taha's sperm count was 15 times lower. Concerned, he chose to do a second analysis at a different clinic and, to his surprise, the result was far more positive: 20 m/ml.
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.