Health & Medicine


Artificial Intelligence Research by IDTechEx

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Researchers have figured out how to use deep learning to speed up the analysis of gas chromatographic data. Because this type of analysis is used in many parts of society, the new method will have a major impact on quality, efficiency and cost when examining various data -- from blood tests, to the fermentation of cheese. Gas chromatography is a method of analysis that most people have experienced at one time or another without necessarily knowing it. For example, gas chromatography can be used to reveal food fraud, find out where a particular batch of cocaine was produced or monitor a fermentation of cheese. "The new interpretive method of gas chromatographic analysis can make this type of analysis accessible to many more, which means that better and cheaper decisions can be made in a number of areas in society," says Professor Rasmus Bro, Department of Food Science at the University of Copenhagen (UCPH FOOD), who is one of the researchers behind the new interpretive method.


AI-Powered Chatbot App on Mobile and Desktop Screens Can Help the Homeless

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A new AI-(artificial intelligence) powered mobile and desktop chatbot app helps homeless people find the services and supports they need. The free, 24/7 support service puts real-time information about free meals, clothing banks, overnight shelters, drop-in facilities and more on the screens of desktop computers and mobile phones through a simple, scripted question-and-answer text box format. Called Chalmers, the chatbot also provides emergency crisis lines (other than 911) so people can get help with domestic abuse cases or mental health concerns. Unfortunately, the rapid adoption of Chalmers demonstrates an immediate and ever-increasing need for its service. In 2016, an estimated 133,000 people "experienced homelessness", according to Employment and Social Development Canada (ESDC).


Workshop on "High Content Imaging and Data Science for Virtual Screening and Drug Discovery", Bled 2019

VideoLectures.NET

High-throughput phenotypic screening, based on high content imaging, is increasingly often used as a tool in the context of drug discovery. Compound screens are used to find hits that produce the desired phenotypes in relevant cellular assays. Genomic screens are used to elucidate the underlying molecular pathways and identify suitable drug targets. Since a wealth of data is produced in the process of high- content screening, data science approaches such as statistics, machine learning and neural networks can play an important role in making the most of the collected data. Much like virtual screening can be performed in more classical chemoinformatic settings by, e.g., learning predictive models for QSAR (quantitative structure-activity relations) from data obtained through compound screens, similar approaches can be taken in the context of high-throughput phenotypic screening.


IVF ID

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In vitro fertilization (IVF) technology has made enormous strides in recent decades, resulting in millions of successful pregnancies. Nevertheless, disparity in visual morphology assessment results between embryologists has raised serious questions about the efficacy of embryo selection (1). Attempting to solve this problem, Iman Hajirasouliha (Assistant Professor of Computational Biology) and his team at Cornell University the Englander Institute for Precision Medicine at Weill Cornell Medicine have developed a new tool – the aptly-named STORK – which is capable of driving robust assessment and selection of human blastocysts (2). "Often, to overcome uncertainties in embryo quality, many more embryo's than required are implanted into the patient," says Hajirasouliha. "Often, this leads to undesired multiple pregnancies and serious complications." Convinced there must be a more efficient way to screen for healthy, viable embryos, the team trained a deep neural network to select the best embryo's using time-stamped images of embryos from a large IVF center.


Inside the Mind and Methodology of a Data Scientist - Birst

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When you hear about Data Science, Big Data, Analytics, Artificial Intelligence, Machine Learning, or Deep Learning, you may end up feeling a bit confused about what these terms mean. And it doesn't help reduce the confusion when every tech vendor rebrands their products as AI. So, what do these terms really mean? What are overlaps and differences? And most importantly, what can this do for your business?


University of Alberta PhD student develops AI to identify depression

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Our voices may convey subtle clues about our mood and psychological state. Now, scientists are using artificial intelligence to pick up these clues, with the aim of building voice-analyzing technologies that can identify individuals in need of mental-health care. But others caution they could do more harm than good. At the University of Alberta, computing science PhD student Mashrura Tasnim has developed a machine-learning model that can recognize the speech qualities of people with depression. Her goal is to create a smartphone application that would monitor users' conversations and alert their emergency contacts or mental-health professionals when it detects depression.


Artificial intelligence to predict protein structure

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Proteins are biological high-performance machines. They can be found in every cell and play an important role in human blood coagulation or as main constituents of hairs or muscles. The function of these molecular tools is obvious from their structure. Researchers of Karlsruhe Institute of Technology (KIT) have now developed a new method to predict this protein structure with the help of artificial intelligence. This is very difficult to detect, the experiments needed for this purpose are expensive and complex.


Health system execs eyeing AI investments, but lack vendor knowledge

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Adoption and investment in artificial intelligence and robotic process automation is still in its early growth stage in the healthcare industry, with just half of hospital leaders familiar with the technologies. WHY IT MATTERS These were among the results of a survey of 115 executives at hospital systems and independent hospitals in the United States, conducted by healthcare digitization vendor Olive and market research firm Sage Growth Partners. The study also found that nearly a quarter (23 percent) of health system executives are looking to invest in the two technologies today, and half said they plan to do so within the next two years. The top reasons cited for deploying AI technology included improving efficiency and reducing costs, improving the quality of care and improving patient satisfaction and engagement. While interest in AI and RPA technology is growing, the survey results also indicated that there is a lack of general knowledge as to where to procure the solutions or what vendors offer them, with more than half of survey respondents unable to name an AI or RPA vendor or solution.



AI could solve the healthcare staffing crisis and become our radiologists of the future

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It is almost 40 years since a full-body magnetic resonance imaging (MRI) machine was used for the first time to scan a patient and generate diagnostic-quality images. The scanner and signal processing methods needed to produce an image were devised by a team of medical physicists including John Mallard, Jim Hutchinson, Bill Edelstein and Tom Redpath at the University of Aberdeen, leading to the widespread use of the MRI scanner, now a ubiquitous tool in radiology departments across the world. MRI was a game-changer in medical diagnostics because it didn't require exposure to ionising radiation (such as X-rays), and could generate images on multiple cross-sections of the body with superb definition of soft tissues. This allowed, for example, the direct visualisation of the spinal cord for the first time. Most people today will have undergone an MRI or know somebody who has.