Lifeboat Foundation

In recent years, researchers have been developing machine learning algorithms for an increasingly wide range of purposes. This includes algorithms that can be applied in healthcare settings, for instance helping clinicians to diagnose specific diseases or neuropsychiatric disorders or monitor the health of patients over time.

Researchers at Massachusetts Institute of Technology (MIT) and Massachusetts General Hospital have recently carried out a study investigating the possibility of using deep reinforcement learning to control the levels of unconsciousness of patients who require anesthesia for a medical procedure. Their paper, set to be published in the proceedings of the 2020 International Conference on Artificial Intelligence in Medicine, was voted the best paper presented at the conference.

“Our lab has made significant progress in understanding how anesthetic medications affect neural activity and now has a multidisciplinary team studying how to accurately determine anesthetic doses from neural recordings,” Gabriel Schamberg, one of the researchers who carried out the study, told TechXplore. “In our recent study, we trained a neural network using the cross-entropy method, by repeatedly letting it run on simulated patients and encouraging actions that led to good outcomes.”

Microsoft has made several quirky and useful apps that can help you with daily problems and its new app seeks to help you with math.

Microsoft Math Solver — available on both iOS and Android — can solve various math problems including quadratic equations, calculus, and statistics. The app can also show graphs for the equation to enhance your understanding of the subject.

Ira Pastor, ideaXme life sciences ambassador and founder of Bioquark interviews Dr Vitaly Vanchurin, PhD, Associate Professor, Theoretical Physics and Cosmology, Swenson College of Science and Engineering, at the University of Minnesota (UMN).

Dr Vanchurin’s big questions and the tools we need to answer them:

Continue reading “Big Questions: The Multiverse, Cosmological Neural Networks and ‘Space Noodles’” »

While critically ill patients experience a life-threatening illness, they commonly contract ventilator-associated pneumonia. This nosocomial infection increases morbidity and likely mortality as well as the cost of health care. This article reviews the literature with regard to diagnosis, treatment, and prevention. It provides conclusions that can be implemented in practice as well as an algorithm for the bedside clinician and also focuses on the controversies with regard to diagnostic tools and approaches, treatment plans, and prevention strategies.

Patients in the intensive care unit (ICU) are at risk for dying not only from their critical illness but also from secondary processes such as nosocomial infection. Pneumonia is the second most common nosocomial infection in critically ill patients, affecting 27% of all critically ill patients (170). Eighty-six percent of nosocomial pneumonias are associated with mechanical ventilation and are termed ventilator-associated pneumonia (VAP). Between 250,000 and 300,000 cases per year occur in the United States alone, which is an incidence rate of 5 to 10 cases per 1,000 hospital admissions (134, 170). The mortality attributable to VAP has been reported to range between 0 and 50% (10, 41, 43, 96, 161).

How much control do you have over your thoughts? What if you were specifically told not to think of something—like a pink elephant?

A recent study led by UNSW psychologists has mapped what happens in the brain when a person tries to suppress a thought. The neuroscientists managed to ‘decode’ the complex brain activity using functional brain imaging (called fMRI) and an imaging algorithm.

The findings suggest that even when a person succeeds in ignoring a thought, like the pink elephant, it can still exist in another part of the brain—without them being aware of it.

TL;DR: This article walks through the mechanism of a popular machine learning algorithm called neural style transfer (NST), which is able…

NASA is going to be testing a new precision landing system designed for use on the tough terrain of the moon and Mars for the first time during an upcoming mission of Blue Origin’s New Shepard reusable suborbital rocket. The “Safe and Precise Landing – Integrated Capabilities Evolution” (SPLICE) system is made up of a number of lasers, an optical camera and a computer to take all the data collected by the sensors and process it using advanced algorithms, and it works by spotting potential hazards, and adjusting landing parameters on the fly to ensure a safe touchdown.

SPLICE will get a real-world test of three of its four primary subsystems during a New Shepard mission to be flown relatively soon. The Jeff Bezos –founded company typically returns its first-stage booster to Earth after making its trip to the very edge of space, but on this test of SPLICE, NASA’s automated landing technology will be operating on board the vehicle the same way they would when approaching the surface of the moon or Mars. The elements tested will include “terrain relative navigation,” Doppler radar and SPLICE’s descent and landing computer, while a fourth major system — lidar-based hazard detection — will be tested on future planned flights.

Currently, NASA already uses automated landing for its robotic exploration craft on the surface of other planets, including the Perseverance rover headed to Mars. But a lot of work goes into selecting a landing zone with a large area of unobstructed ground that’s free of any potential hazards in order to ensure a safe touchdown. Existing systems can make some adjustments, but they’re relatively limited in that regard.

Researchers at the Samsung AI Center in Moscow (Russia) have recently presented interesting work called Living portraits: they made Mona Lisa and other subjects of photos and art alive using video of real people. They presented a framework for meta-learning of adversarial generative models called “Few-Shot Adversarial Learning”.

You can read more about details in the original paper.

Here we review this great implementation of the algorithm in PyTorch. The author of this implementation is Vincent Thévenin — research worker in De Vinci Innovation Center.

September 14, 2020 — The use of artificial intelligence (AI) in radiology to aid in image interpretation tasks is evolving, but many of the old factors and concepts from the computer-aided detection (CAD) era still remain, according to a Sunday talk at the Conference on Machine Intelligence in Medical Imaging (C-MIMI).

A lot has changed as the new era of AI has emerged, such as faster computers, larger image datasets, and more advanced algorithms — including deep learning. Another thing that’s changed is the realization of additional reasons and means to incorporate AI into clinical practice, according to Maryellen Giger, PhD, of the University of Chicago. What’s more, AI is also being developed for a broader range of clinical questions, more imaging modalities, and more diseases, she said.

At the same time, many of the issues are the same as those faced in the era of CAD. There are the same clinical tasks of detection, diagnosis, and response assessment, as well as the same concern of “garbage in, garbage out,” she said. What’s more, there’s the same potential for off-label use of the software, and the same methods for statistical evaluations.