At HIMSS20 next month, two machine learning experts will show how machine learning algorithms are evolving to handle complex physiological data and drive more detailed clinical insights. During surgery and other critical care procedures, continuous monitoring of blood pressure to detect and avoid the onset of arterial hypotension is crucial. New machine learning technology developed by Edwards Lifesciences has proven to be an effective means of doing this. In the prodromal stage of hemodynamic instability, which is characterized by subtle, complex changes in different physiologic variables unique dynamic arterial waveform "signatures" are formed, which require machine learning and complex feature extraction techniques to be utilized. Feras Hatib, director of research and development for algorithms and signal processing at Edwards Lifesciences, explained his team developed a technology that could predict, in real-time and continuously, upcoming hypotension in acute-care patients, using an arterial pressure waveforms.
Image classification is the Hello World of deep learning. For me, that project was Pneumonia Detection using Chest X-rays. Since this was a relatively small dataset, I could train my model in about 50 minutes. The dataset I worked with, involved around 4,500 images. And the only reason it took 50 minutes was because the images were high definition.
A Japanese medical advice app provider is making a limited time offer of a free app that allows users to seek advice from doctors about the coronavirus. The free service, in Japanese only, is provided by Agree, a company based in Tsukuba, Ibaraki Prefecture. It also operates a medical advice app called Leber. Users are asked to send information such as whether they have traveled to any places where COVID-19 has been confirmed or whether they have developed a fever. With about 120 doctors registered for the service, users receive advice in about 30 minutes about the urgency of their condition, such as if they are suspected of having pneumonia and if they should seek advice from a public health center.
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Deep Learning: Deep Learning is a subset of device machine learning and artificial intelligence with a few algorithms referred by the shape and feature of the brain known as artificial neural networks. Deep Learning may be supervised, semi-supervised or unsupervised. Machine Learning: Machine learning is a Subset of artificial intelligence (AI) that allow systems the ability to automatically learn and improve from previous event without being leaving programmed. Set of instruction build a mathematical model based on sample data, known as "training data". Artificial Neural Networks: Artificial neural networks (ANN) also known as as connectionist structures are computing structures slightly referred through the biological neural networks that are present in human brains.
AI has a lot of fantastic use cases. In fact, it really can help us out in so many interesting ways. However, its utility is largely down to what humans decide for it – and we can be total idiots. Let's take a look at the 5 most unethical AI projects to date. Kalashnikov is a Russian weapons manufacturer, known for their (frankly) insane products.
"Between 12 to 18 million Americans every year will experience some sort of diagnostic error," said Paul Cerrato, a journalist and researcher. "So the question is: Why such a huge number? And what can we do better in terms of reinventing the tools so they catch these conditions more effectively?" Cerrato is co-author, alongside Dr. John Halamka, newly minted president of Mayo Clinic Platform, of the new HIMSS Book Series edition, Reinventing Clinical Decision Support: Data Analytics, Artificial Intelligence, and Diagnostic Reasoning. At HIMSS20, the two of them will discuss the book, and the bigger picture around CDS tools that are fast being transformed by the advent of artificial intelligence, machine learning and big data analytics.
In a supervised learning setting, we have a yardstick or plumbline to judge how well we are doing: the response itself. A frequent question in biological and biomedical applications is whether a property of interest (say, disease type, cell type, the prognosis of a patient) can be "predicted", given one or more other properties, called the predictors. Often we are motivated by a situation in which the property to be predicted is unknown (it lies in the future, or is hard to measure), while the predictors are known. The crucial point is that we learn the prediction rule from a set of training data in which the property of interest is also known. Once we have the rule, we can either apply it to new data, and make actual predictions of unknown outcomes; or we can dissect the rule with the aim of better understanding the underlying biology. Compared to unsupervised learning and what we have seen in Chapters 5, 7 and 9, where we do not know what we are looking for or how to decide whether our result is "right", we are on much more solid ground with supervised learning: the objective is clearly stated, and there are straightforward criteria to measure how well we are doing. The central issues in supervised learning151151 Sometimes the term statistical learning is used, more or less exchangeably. Or did our rule indeed pick up some of the pertinent patterns in the system being studied, which will also apply to yet unseen new data? An example for overfitting: two regression lines are fit to data in the \((x, y)\)-plane (black points). We can think of such a line as a rule that predicts the \(y\)-value, given an \(x\)-value. Both lines are smooth, but the fits differ in what is called their bandwidth, which intuitively can be interpreted their stiffness. The blue line seems overly keen to follow minor wiggles in the data, while the orange line captures the general trend but is less detailed. The effective number of parameters needed to describe the blue line is much higher than for the orange line. Also, if we were to obtain additional data, it is likely that the blue line would do a worse job than the orange line in modeling the new data. We'll formalize these concepts –training error and test set error– later in this chapter. Although exemplified here with line fitting, the concept applies more generally to prediction models. See exemplary applications that motivate the use of supervised learning methods.
While much of the literature and buzz on deep learning concerns computer vision and natural language processing(NLP), audio analysis -- a field that includes automatic speech recognition(ASR), digital signal processing, and music classification, tagging, and generation -- is a growing subdomain of deep learning applications. Some of the most popular and widespread machine learning systems, virtual assistants Alexa, Siri, and Google Home, are largely products built atop models that can extract information from audio signals. Audio data analysis is about analyzing and understanding audio signals captured by digital devices, with numerous applications in the enterprise, healthcare, productivity, and smart cities. Applications include customer satisfaction analysis from customer support calls, media content analysis and retrieval, medical diagnostic aids and patient monitoring, assistive technologies for people with hearing impairments, and audio analysis for public safety. In the first part of this article series, we will talk about all you need to know before getting started with the audio data analysis and extract necessary features from a sound/audio file. We will also build an Artificial Neural Network(ANN) for the music genre classification.
Significant technological advancements and societal shifts occurred during the 2010's decade. Yet many of these developments became so quickly engrained in our daily lives that they often went relatively unnoticed, and their impact all but forgotten. Over this next decade, the 2020s, we expect similar rapid and meaningful advancements to occur. Moore's law suggests that over a 10-year period, semiconductors will advance by 32 times, bringing about mesmerizing innovation in the digital age that should not only change technology but society as well. In this piece, we review the technological advancements over the last decade and anticipate what revolutionary changes may be in store for us over the next 10 years.