Tag: Machine Learning

Low-Cost Disease Diagnosis by Mapping Heart Sounds

sarah JonasResearch Results

In the Journal of Applied Physics, researchers develop a method to identify aortic valve dysfunction using complex network analysis that is accurate, simple to use, and low-cost. They used heart sound data to create a complex network of connected points, which was split into sections, and each part was represented with a node. If the sound in two portions was similar, a line was drawn between them. In a healthy heart, the graph showed two distinct clusters of points, with many nodes unconnected. A heart with aortic stenosis contained many more correlations and edges.

Preventing Pressure Injuries Among ICU Patients With COVID-19 Requires Extra Vigilance

sarah JonasResearch Results

Patients who are critically ill with COVID-19 are at exceptionally high risk for developing healthcare-associated pressure injuries, especially those related to medical devices, and clinicians must consider additional factors beyond those assessed with common classification tools.

Precision health perspectives

sarah JonasFeature Expert

In February, UCI launched the Institute for Precision Health, a campus-wide, interdisciplinary endeavor that merges UCI’s powerhouse health sciences, engineering, machine learning, artificial intelligence, clinical genomics and data science capabilities. The objective is to identify, create and deliver the most effective health and wellness strategy for each individual person and, in doing so, confront the linked challenges of health equity and the high cost of care.

Rensselaer Researchers to Address Big Data Challenges

sarah JonasAnnouncement

Dr. Yangyang Xu, assistant professor of mathematical sciences at Rensselaer Polytechnic Institute, has received a $250,000 grant from the National Science Foundation (NSF) to research challenges associated with distributed big data in machine learning.Machine learning algorithms allow computers to make decisions, predictions, and recommendations on the basis of input training data without being explicitly told what information to look for in the data.

Get More from Your Lunch Break with Bite-Size Science

sarah JonasAnnouncement

Take a break for lunch and nourish your brain with the latest in scientific discussions, presented by experts at Jefferson Lab. The second season of the lab’s summer series, Bite-Size Science, is now underway. The Bite-Size Science lunchtime lecture series features half-hour, live-streamed presentations on lab-related science, engineering and technology topics and presented by leaders in their fields. The presentations are tailored to non-scientists and are brief, free, and feature a chat feature for Q&A with the presenters.

UCLA researchers use artificial intelligence tools to speed critical information on drug overdose deaths

sarah JonasResearch Results

Fast data processing of overdose deaths, which have increased in recent years, is crucial to developing a rapid public health response. But the system now in place lacks precision and takes months. To correct that, UCLA researchers have developed an automated process that reduces data collection to a few weeks.

JMIR Biomedical Engineering | Using Machine Learning to Reduce Treatment Burden

sarah JonasResearch Results

JMIR Publications recently published “Reducing Treatment Burden Among People With Chronic Conditions Using Machine Learning: Viewpoint” in JMIR Biomedical Engineering which reported that the COVID-19 pandemic has illuminated multiple challenges within the health care system and is unique to those living with chronic conditions.

SLAC expands and centralizes computing infrastructure to prepare for data challenges of the future

sarah JonasFeature

A computing facility at the Department of Energy’s SLAC National Accelerator Laboratory is doubling in size, preparing the lab for new scientific endeavors that promise to revolutionize our understanding of the world from atomic to cosmic scales but also require handling unprecedented data streams.

Machine Learning Paves Way for Smarter Particle Accelerators

sarah JonasResearch Results

Scientists have developed a new machine-learning platform that makes the algorithms that control particle beams and lasers smarter than ever before. Their work could help lead to the development of new and improved particle accelerators that will help scientists unlock the secrets of the subatomic world.

Global Expert Panel Identifies 5 Areas Where Machine Learning Could Enhance Health Economics and Outcomes Research

sarah JonasResearch Results

Value in Health, the official journal of ISPOR—the professional society for health economics and outcomes research, announced today the publication of new guidance for health economics and outcomes research and decision makers in the use of an important class of artificial intelligence techniques.

Designed to identify wildlife by sound, the BirdNET app opens new avenues for citizen science

sarah JonasResearch Results

The BirdNET app, a free machine-learning powered tool that can identify more than 3,000 birds by sound alone, generates reliable scientific data and makes it easier for people to contribute citizen-science data on birds by simply recording sounds. Results of tests to measure the app’s accuracy are published in the open access journal PLOS Biology.

Sentience is beside the point when it comes to AI & ethics

sarah JonasExpert Pitch

Yevgeniy Vorobeychik doesn’t know exactly what sentience is. Since he’s an engineer, not a philosopher, Vorobeychik can’t say what it’s like to be a bat  or a tree or a rock. He can’t quantify the importance of embodiment to consciousness. He’s not even sure that there’s an inherent problem with people reacting to an artificial intelligence in ways similar to how they react to other people.

Mount Sinai Researchers Develop Machine Learning Model that Can Detect and Predict COVID-19 from Collected Data on Wearable Devices

sarah JonasResearch Results

Mount Sinai researchers have developed a machine learning algorithm that can determine if an individual has SARS-CoV-2 infections, the virus that causes COVID-19—with a high sensitivity and specificity—from the data collected by wearable devices.

Computer hardware mimics brain functions

sarah JonasResearch Results

A multi-institutional team, including Argonne National Laboratory, has developed a material with which computer chips can be designed to reconfigure their circuits when presented with new information. It does so by mimicking functions in the human brain.

Machine Learning Framework IDs Targets for Improving Catalysts

sarah JonasResearch Results

Chemists at the U.S. Department of Energy’s Brookhaven National Laboratory have developed a new machine-learning (ML) framework that can zero in on which steps of a multistep chemical conversion should be tweaked to improve productivity. The approach could help guide the design of catalysts — chemical “dealmakers” that speed up reactions.

VA, ORNL and Harvard develop novel method to identify complex medical relationships

sarah JonasResearch Results

A team of researchers from the Department of Veterans Affairs, Oak Ridge National Laboratory, Harvard’s T.H. Chan School of Public Health, Harvard Medical School and Brigham and Women’s Hospital has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.

AI Could Predict Ideal Chronic Pain Patients for Spinal Cord Stimulation

sarah JonasResearch Results

Spinal cord stimulation is a minimally invasive FDA-approved treatment to manage chronic pain such as back and neck pain. The ability to accurately predict which patients will benefit from this treatment in the long term is unclear and currently relies on the subjective experience of the implanting physician. A study is the first to use machine-learning algorithms in the neuromodulation field to predict long-term patient response to spinal cord stimulation.

New brain learning mechanism calls for revision of long-held neuroscience hypothesis

sarah JonasResearch Results

In an article published today in Scientific Reports (https://www.nature.com/articles/s41598-022-10466-8), researchers from Bar-Ilan University in Israel reveal that the brain learns completely differently than has been assumed since the 20th century. The new experimental observations suggest that learning is mainly performed in neuronal dendritic trees, where the trunk and branches of the tree modify their strength, as opposed to modifying solely the strength of the synapses (dendritic leaves), as was previously thought. These observations also indicate that the neuron is actually a much more complex, dynamic and computational element than a binary element that can fire or not. Just one single neuron can realize deep learning algorithms, which previously required an artificial complex network consisting of thousands of connected neurons and synapses. The new demonstration of efficient learning on dendritic trees calls for new approaches in brain research, as well as for the generation

Scientists use machine learning to identify antibiotic resistant bacteria that can spread between animals, humans and the environment

sarah JonasResearch Results

Experts from the University of Nottingham have developed a ground-breaking software, which combines DNA sequencing and machine learning to help them find where, and to what extent, antibiotic resistant bacteria is being transmitted between humans, animals and the environment.

Matt Ajemian, Ph.D., Receives Prestigious NSF CAREER Award

sarah JonasAnnouncement

Matt Ajemian, Ph.D., has received a $1,103,081 NSF CAREER grant for a project that will build fundamental knowledge on where and when large shell-crushing predators feed in order to ensure a sustainable future for shellfish species. Further, the work can provide guidance to shellfish restoration programs that are currently “flying blind” with respect to predation risk.

AF2Complex: Researchers Leverage Deep Learning to Predict Physical Interactions of Protein Complexes

sarah JonasResearch Results

Proteins are the molecular machinery that makes life possible, and researchers have long been interested in a key trait of protein function: their three-dimensional structure. A new study by Georgia Tech and Oak Ridge National Laboratory details a computational tool able to predict the structure protein complexes – and lends new insights into the biomolecular mechanisms of their function.

Machine Learning Helps Predict Protein Functions

sarah JonasResearch Results

To engineer proteins for specific functions, scientists change a protein sequence and experimentally test how that change alters its function. Because there are too many possible amino acid sequence changes to test them all in the laboratory, researchers build computational models that predict protein function based on amino acid sequences. Scientists have now combined multiple machine learning approaches for building a simple predictive model that often works better than established, complex methods.

Artificial intelligence paves the way to discovering new rare-earth compounds

sarah JonasResearch Results

Artificial intelligence advances how scientists explore materials. Researchers from Ames Laboratory and Texas A&M University trained a machine-learning (ML) model to assess the stability of rare-earth compounds. The framework they developed builds on current state-of-the-art methods for experimenting with compounds and understanding chemical instabilities.