Health & Medicine


Secure and Robust Machine Learning for Healthcare: A Survey

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Medical ML/DL system shall facilitate a deep understanding of the underlying healthcare task, which (in most cases) can only be achieved by utilising other forms of patients data. For example, radiology is not all about clinical imaging. Other patient EMR data is crucial for radiologists to derive the precise conclusion for an imaging study. This calls for the integration and data exchange between all healthcare systems. Despite extensive research on data exchange standards for healthcare, there is a huge ignorance in following those standards in healthcare IT systems which broadly affects the quality and efficacy of healthcare data, accumulated through these systems.


Biomedical Image Segmentation: Attention U-Net

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Medical image segmentation has been actively studied to automate clinical analysis. Deep learning models generally require a large amount of data, but acquiring medical images is tedious and error-prone. Attention U-Net aims to automatically learn to focus on target structures of varying shapes and sizes; thus, the name of the paper "learning where to look for the Pancreas" by Oktay et al. U-Nets are commonly used for image segmentation tasks because of its performance and efficient use of GPU memory. It aims to achieve high precision that is reliable for clinical usage with fewer training samples because acquiring annotated medical images can be resource-intensive.


Poor data is hindering machine learning, US drug development, study says: A lack of proper data is hurting the use of machine learning to develop drugs, which could put U.S. drugmakers at a competitive disadvantage compared to other countries, according to a report from the U.S. Government Accountability Office and the National Academy of Medicine.

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A lack of proper data is hurting the use of machine learning to develop drugs, which could put U.S. drugmakers at a competitive disadvantage compared to other countries, according to a report from the U.S. Government Accountability Office and the National Academy of Medicine. Machine learning is a type of artificial intelligence that involves using data to train computers to make decisions and learn from experiences, according to Pharmaphorum. It has the potential to cut costs of research and development for drugmakers by helping researchers to predict what will and won't work in clinical trials. However, the report says a lot of the data being used in drug development is not suitable for machine learning purposes. There is a phenomenon known as "garbage in, garbage out," where a machine learning system can't produce credible results because of poor data, according to Pharmaphorum.


15 PhD positions in physics, materials science, chemistry, computer science, mathematics, artificial intelligence and/or electrical engineering

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Apply for a position in our exciting research on "Materials for Neuromorphic Circuits" (MANIC), and become part of the next generation of neuromorphic experts! Funded by the European Commission through the Horizon 2020 Marie Sklodowska-Curie ITN Programme, the MANIC network offers 15 high level fellowships for joint research on new materials for cognitive applications. The most talented and motivated students will be selected for advanced multidisciplinary research training, preferably starting July 2020. The scientific aim of MANIC is to synthesize materials that can function as networks of neurons and synapses by integrating conductivity, plasticity and self-organization. Successes in deep learning show that the paradigm of neuromorphic computing is very attractive.


Top Quotes about AI, Automation and Robotics - Supply Chain Today

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"Artificial intelligence is growing up fast, as are robots whose facial expressions can elicit empathy and make your mirror neurons quiver." "In the long term, artificial intelligence and automation are going to be taking over so much of what gives humans a feeling of purpose." "I predict that, because of artificial intelligence and its ability to automate certain tasks that in the past were impossible to automate, not only will we have a much wealthier civilization, but the quality of work will go up very significantly and a higher fraction of people will have callings and careers relative to today." "Let's start with the three fundamental Rules of Robotics…. We have: one, a robot may not injure a human being, or, through inaction, allow a human being to come to harm. Two, a robot must obey the orders given it by human beings except where such orders would conflict with the First Law. And three, a robot must protect its own existence as long as such protection does not conflict with the First or Second Laws." "In 30 years, a robot will likely be on the cover of time magazine as the best CEO. Machines will do what human beings are incapable of doing. Machines will partner and cooperate with humans, rather than become mankind's biggest enemy."


Top Quotes about AI, Automation and Robotics - Supply Chain Today

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"Artificial intelligence is growing up fast, as are robots whose facial expressions can elicit empathy and make your mirror neurons quiver." "In the long term, artificial intelligence and automation are going to be taking over so much of what gives humans a feeling of purpose." "I predict that, because of artificial intelligence and its ability to automate certain tasks that in the past were impossible to automate, not only will we have a much wealthier civilization, but the quality of work will go up very significantly and a higher fraction of people will have callings and careers relative to today." "Let's start with the three fundamental Rules of Robotics…. We have: one, a robot may not injure a human being, or, through inaction, allow a human being to come to harm. Two, a robot must obey the orders given it by human beings except where such orders would conflict with the First Law. And three, a robot must protect its own existence as long as such protection does not conflict with the First or Second Laws." "In 30 years, a robot will likely be on the cover of time magazine as the best CEO. Machines will do what human beings are incapable of doing. Machines will partner and cooperate with humans, rather than become mankind's biggest enemy."


New study examines mortality costs of air pollution in US

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A team of University of Illinois researchers estimated the mortality costs associated with air pollution in the U.S. by developing and applying a novel machine learning-based method to estimate the life-years lost and cost associated with air pollution exposure. Scholars from the Gies College of Business at Illinois studied the causal effects of acute fine particulate matter exposure on mortality, health care use and medical costs among older Americans through Medicare data and a unique way of measuring air pollution via changes in local wind direction. The researchers - Tatyana Deryugina, Nolan Miller, David Molitor and Julian Reif - calculated that the reduction in particulate matter experienced between 1999-2013 resulted in elderly mortality reductions worth $24 billion annually by the end of that period. Garth Heutel of Georgia State University and the National Bureau of Economic Research was a co-author of the paper. "Our goal with this paper was to quantify the costs of air pollution on mortality in a particularly vulnerable population: the elderly," said Deryugina, a professor of finance who studies the health effects and distributional impact of air pollution.


An AI Epidemiologist Sent the First Warnings of the Wuhan Virus

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On January 9, the World Health Organization notified the public of a flu-like outbreak in China: a cluster of pneumonia cases had been reported in Wuhan, possibly from vendors' exposure to live animals at the Huanan Seafood Market. The US Centers for Disease Control and Prevention had gotten the word out a few days earlier, on January 6. But a Canadian health monitoring platform had beaten them both to the punch, sending word of the outbreak to its customers on December 31. BlueDot uses an AI-driven algorithm that scours foreign-language news reports, animal and plant disease networks, and official proclamations to give its clients advance warning to avoid danger zones like Wuhan. Speed matters during an outbreak, and tight-lipped Chinese officials do not have a good track record of sharing information about diseases, air pollution, or natural disasters.


How artificial intelligence provided early warnings of the Wuhan virus

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During the kind of virus outbreak that China and other nations are now contending with, time is of the essence. Such was the case in 2002 and 2003, when Chinese authorities were accused of covering up the SARS epidemic that eventually claimed over 740 lives around the world. With the current outbreak, involving a coronavirus that originated in Wuhan and has so far taken over 40 lives, the Chinese government is being more transparent, as Germany's health minister noted to Bloomberg yesterday on the sidelines of the World Economic Forum in Davos.


Brain tech is coming of age, but will it make you smarter?

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With Brain Computer Interface (BCI), scientists and medical practitioners are already helping people with damaged limbs, eyes or ears regain sensations like touch, grasp, eyesight and hearing, by recording specific brain signals and translating them into actions. BCI exists in two forms: the first is the non-invasive, which includes devices or electrodes that are worn on the body. The second is the invasive variant where an electrode is implanted under the scalp so it can record information directly from the neurons in the brain. "The moment you talk about the invasive variety, it opens up a lot of possibilities. We have always had cochlear implants for cornea and ear, and pacemakers for heart. They have been fairly successful. Invasive variety can never be a mass product as it has to be entered carefully in a sophisticated medical environment only," points out Kumaar Bagrodia, founder and chief executive officer of NeuroLeap, a neuroscience startup.