Articles tagged with Machine Learning
Researchers developed an AI model that uses daily step counts to predict the likelihood of unplanned hospitalization during cancer therapy, offering personalized care and potential prevention of treatment interruptions. The model was tested on data from wearable consumer devices and showed strong predictive performance.
A novel algorithm uses near-infrared spectroscopy to estimate intracranial pressure (ICP) based on hemoglobin levels. The research validates the accuracy of this method using invasive ICP data.
Researchers developed a machine learning-based diagnostic method that combines genomic sequencing and analysis of patients' immune response for remarkable accuracy. The approach identifies and predicts sepsis cases with high accuracy, potentially exceeding current diagnostic capabilities.
Researchers have developed a novel method for antibiotic resistance testing that can analyze bacterial cells in real-time, allowing for faster identification of susceptible and resistant bacteria. This breakthrough technology has the potential to transform microbial screening in clinical and research labs.
The platform enhances neurosurgical procedures by delivering precise treatment and diagnosis in deep brain tissue. Researchers successfully implanted the catheter in live sheep without damage or infection, paving the way for potential human trials within four years.
Researchers have developed a deep learning algorithm that can accurately assess the stage of head and neck cancer using standard CT scans, outperforming expert radiologists. The algorithm demonstrated superior accuracy in measuring the extent of cancer spread, especially for patients with high-risk disease.
Researchers at MIT have developed a new method that uses optics to accelerate machine-learning computations on low-power devices. By encoding model components onto light waves, data can be transmitted rapidly and computations performed quickly, leading to over a hundredfold improvement in energy efficiency.
Researchers developed an AI-based model that combines artificial intelligence and weather forecast models to predict extreme wildfire danger with high accuracy. The new method can produce forecasts of extreme fire danger out to one week at finer scales (4km x 4km resolution), increasing its utility for fire suppression and management.
Researchers found that content overlap between critic and user reviews increases movie demand, particularly for movies with mediocre review ratings. The study suggests that producers and marketers can leverage this by engaging with both professional critics and general consumers to find commonalities in their reviews.
Researchers use machine learning to generate beta-barrel proteins that can detect metal ions in water, a potential solution for identifying pollutants like lead in waterways. The 'deep-fake' protein is designed to be ideal for binding specific metals and creating conductance differences.
FathomNet aggregates images from multiple sources to create a publicly available, expertly curated underwater image training database. The database uses artificial intelligence and machine learning to alleviate the bottleneck for analyzing underwater imagery, accelerating important research around ocean health.
A new AI app improved the accuracy of COVID-19 lateral flow test results, increasing sensitivity from 92% to 97.6%. The app was tested at UK Health Security Agency assisted test centers and found promising results in reducing false negatives.
A new AI model called CODE-AE can accurately predict the efficacy of novel drug compounds in humans, addressing a major challenge in drug discovery. The technique has been shown to improve accuracy and robustness over state-of-the-art methods, with potential applications in personalized medicine.
Omnipose, a deep learning software, can identify various types of tiny objects in micrographs with high precision, including bacteria of all shapes and sizes. It overcomes limitations of previous approaches by handling object overlap and detecting cell intoxication, making it a game-changer for biological image analysis.
A University of Groningen team created two machine learning models to predict app removal risks, achieving accuracy rates of up to 79.2%. The models can help developers avoid bans and users protect their data.
Researchers developed a machine-learning model to predict heat capacity of MOFs, enabling more efficient applications in energy and climate change. The model's accuracy was improved by removing solvent from pores during synthesis.
A recent study analyzed the records of over 33,000 dementia patients in Wales and found that those closest to their diagnosis were taking an average of three or more medications. This suggests a possible link between polypharmacy and the development of dementia.
A study published in the Canadian Journal of Cardiology found that smartwatch health apps detecting atrial fibrillation generated a high rate of false positives and inconclusive results, especially in patients with certain cardiac conditions. Better algorithms and machine learning may help improve the accuracy of these devices.
A team of Illinois Tech researchers used machine learning to estimate the age and gender of individual users with high accuracy, raising questions about data security and privacy. The study highlights the need for better regulations and best practices to protect personal information from being misused.
Researchers successfully taught human and mouse neurons to play the video game Pong in real-time, showcasing their ability to exhibit sentience and adapt to a changing environment. The study's findings have potential applications in disease modeling, drug discoveries, and expanding our understanding of brain function.
Researchers from McGill University and MIT developed an AI system that can learn the rules and patterns of human languages on its own. The model automatically generates higher-level language patterns that can be applied to different languages, achieving better results.
Researchers used AI to analyze seismic signals and predict future fault friction and next failure time with high resolution in laboratory earthquakes. The technique goes beyond previous work by predicting the future state of the fault's physical system.
Researchers developed a smart mouthguard that translates complex bite patterns into instructions to control devices such as computers, smartphones and wheelchairs. The device achieves 98% accuracy and has the potential to support individuals with limited dexterity or neurological disorders.
A University at Buffalo-led research team has been awarded a $5 million grant to develop digital tools that can help older adults recognize and protect themselves from online deceptions. The project, DART, aims to reduce online fraud among older adults, who lose billions of dollars each year due to scams.
Researchers developed a generatively designed patient-specific bone fixation device using Generative Design technology. The implants are tailored to each patient's anatomy and biomechanical needs, resulting in lighter, less prominent, and minimally invasive designs that promote faster healing and reduced revision surgery.
Researchers found that machine learning models outperformed traditional risk prediction models in predicting suicide-related outcomes. These models can identify patterns associated with suicide risk and have been shown to correctly predict 66% of people who would experience a suicide outcome.
Researchers aim to create a unified database network for battery data, facilitating AI analysis and predictions. The Battery Data Genome will collect data across the entire battery lifecycle, from discovery to deployment, with uniform standards for metadata.
Researchers at FAU's Schmidt College of Medicine are developing a pilot program to tackle health disparities by leveraging electronic health records, artificial intelligence, and machine learning. The project aims to improve AI/ML delivery and research operations in community health centers and federally qualified health centers.
A new study found an association between college student FoMO and maladaptive behaviors such as academic misconduct, drug and alcohol use, and breaking the law. Brief FoMO assessments may be valuable risk prediction tools for counselors assisting students in transition to college.
Researchers developed algorithms that can predict volleyball players' actions with over 80% accuracy, combining visual data with hidden variables. The algorithms also showed promise in predicting multiple actions across sequences of up to 44 frames.
Researchers developed a new technique that enables on-device training using less than a quarter of a megabyte of memory, reducing the need for powerful computers and central servers. This approach preserves privacy by keeping data on the device, making deep learning more accessible for low-power edge devices.
Researchers developed a non-invasive algorithm to identify patients with compensated cirrhosis at highest risk for severe complications. The online calculator uses widely available laboratory parameters and is simple, non-invasive, and cost-effective.
Researchers used machine learning algorithms to optimize climate models, increasing their accuracy and detail. By applying Generative Adversarial Networks (GANs) to climate simulations, the team was able to improve the models' ability to represent extreme precipitation events.
The new computer chip uses a transistor-free design that eliminates data transfer time and minimizes energy consumption. It offers up to 100 times faster performance than conventional computing architectures, making it ideal for AI applications.
A machine learning model developed by Carnegie Mellon University can accurately predict how stay-at-home orders affect people with multiple sclerosis. The model uses passively gathered smartphone and fitness tracker data to identify factors that signal changes in health.
A Newcastle University study has developed a machine learning tool that can predict the performance properties of land plant Rubisco proteins with high accuracy. This prediction will enable researchers to identify and engineer 'supercharged' Rubisco proteins that can increase atmospheric CO2 uptake and store in crops such as wheat.
A study published in Cell Press found that when humans are involved, computer decisions are perceived as fairer. Participants deemed decisions related to positive outcomes fairer than negative ones and had concerns over fairness in systems with higher stakes. The results suggest that automated decision-making systems need careful desig...
Researchers developed a machine learning model to quickly recognize predictive risk factors and their importance for undesirable hospitalization outcomes. The model achieved an accuracy of 95.6% and identified modifiable risk factors that can be mitigated through clinical interventions.
Neuronal silencing periods enable efficient temporal sequence identification, allowing the brain to remember phone numbers and PINs. A new AI mechanism utilizing this mechanism also protects against stolen cards by recognizing personal handwriting style and timing.
Researchers at Ohio State University have developed a new machine learning method called next-generation reservoir computing that can learn spatiotemporal chaotic systems in a fraction of the time. The algorithm is more accurate and uses less training data, making it easier to predict complex physical processes like Earth's weather.
A recent grant will fund a project developing new hardware for machine learning, aiming to curb unsustainable energy use in AI systems. The new algorithms being developed are made available to the research community and compatible with an openly shared computing platform.
Researchers developed a computational platform to identify metabolic vulnerabilities in ovarian cancer genes, suggesting opportunities for targeted therapies. The study found that certain genetic alterations can create vulnerabilities in cancer cell metabolism, which can be exploited to selectively kill cancer cells.
Current AI models are restricted by a lack of experience in real-world environments, despite achieving significant advancements in virtual settings. Researchers are now exploring ways to bridge this gap with foundation models that can operate in physical spaces.
A recent study published in Aging-US found that feeling lonely, unhappy, or hopeless increases one's biological age more than smoking. The research used digital models of aging to analyze the effects of various factors on aging rates, revealing a significant correlation between mental health and accelerated aging.
A blood test taken at the time of Covid-19 infection could predict who is most likely to develop long Covid. Researchers identified a 'signature' in protein levels that predicted persistent symptoms after one year.
The use of voice control smart devices in children may impede critical thinking, empathy, and compassion. Devices can't teach children how to behave politely and lack non-verbal communication skills.
Researchers discovered a pattern of DNA mutations that links bladder cancer to tobacco smoking using a powerful new machine learning tool. The tool identified four mutational signatures, including one tied to tobacco smoking, which could lead to more customized treatments for patients with specific cancers.
A machine learning multi-city model is developed to predict municipal solid waste generation in China, using a wide range of feature variables. The model shows good performance with an R-squared value of 0.939 and is suitable for predicting MSW generation in China.
Physicists used machine learning to compress a complex quantum problem into four equations, capturing the physics of electrons on a lattice with high accuracy. The approach could revolutionize how scientists investigate systems containing many interacting electrons and potentially aid in designing materials with sought-after properties.
The 2022 EXPLORE Lunar Data Challenge identifies hazards on the Moon's surface using images from the Lunar Reconnaissance Orbiter. Participants train models to recognize craters and boulders, then create a map of optimal rover routes to avoid hazards.
Researchers use classical computers to make predictions about quantum systems, helping to solve physics and chemistry problems. Machine learning tools provide a bridge between the human world and quantum reality.
Researchers reviewed mobile sensing designs, outcomes, and limitations to better understand its capacity for remote detection, longitudinal tracking, and exposure tracing. Despite technical and societal challenges, advances in data analytics and machine learning may improve data quality and scalability.
A team of scientists from China published a perspective paper on the use of AI in skin diseases, highlighting its potential to assist clinicians. They propose several recommendations to improve AI-assisted diagnosis systems, including establishing a robust database and adapting algorithms to existing real-world databases.
Researchers developed an AI tool using natural language processing and machine learning to identify people who inject drugs in electronic health records. The model accurately identified PWIDs in 1,000 records from 2003-2014, significantly improving clinical decision making and resource allocation.
Researchers developed an electronic laboratory notebook that uses knowledge graphs to describe material properties and experimental processes. The platform enables automated analysis, lossless sharing, and discovery of new materials with potential applications in energy-related devices.
The study used a weakly supervised deep learning algorithm to analyze human brain autopsy tissues and predict the presence or absence of cognitive impairment. The model identified a signal associated with decreasing myelin staining, which was linked to cognitive impairment in the white matter.
A multidisciplinary team at UC San Diego received NSF funding to create a system that remotely monitors patient posture, movement, and physical therapy treatments using wearable technology and machine learning. The goal is to enable personalized physical therapy treatments and improve health outcomes.
Researchers have developed an algorithm that uses smartphone camera and flash to detect low blood oxygen levels. The method produced accurate results in 80% of the participants, showing promise for remote monitoring and early detection of conditions like COVID-19.
Researchers developed a new software tool called ProteinMPNN to create protein molecules more accurately and quickly than before. The team used machine learning algorithms, including AlphaFold, to generate new protein shapes and sequences, paving the way for novel vaccines, treatments, and sustainable biomaterials.
Researchers at Emory University used machine learning and fMRI to analyze a dog's brain activity while watching videos. The results show that dogs are more attuned to actions in their environment than to who or what is performing the action. This study offers proof of concept for decoding canine visual perception.