Machine learning model to predict heart injury post non-cardiac surgery

A prediction model for Myocardial injury after non-cardiac surgery (MINS) was developed by researchers from South Korea using machine learning techniques with the extreme gradient boosting algorithm. The study analyzed 6811 patients who underwent non-cardiac surgery between January 2010 and June 2019. The top 12 variables affecting MINS were preoperative cardiac troponin level, inotropic drug… Continue reading Machine learning model to predict heart injury post non-cardiac surgery

Machine learning model to predict drug side effects

Researchers developed a machine learning model to predict drug side effects that were discovered in post-marketing surveillance after clinical trials. Diego Galeano and Alberto Paccanaro utilized a geometric self-expressive model(GSEM) machine learning framework and post-marketing drug side effect data, drug chemical structure and protein targets data, and drug indications data. GSEM algorithm to predict drug… Continue reading Machine learning model to predict drug side effects

Machine learning algorithm to predict mortality in heart failure patients

Researchers from the University of California San Diego developed a machine-learning model by training a boosted decision tree algorithm on de-identified electronic health records data of 5,822 hospitalized or ambulatory patients with heart failure from the University of California San Diego. This machine learning model is based on eight readily available variables. These include, diastolic… Continue reading Machine learning algorithm to predict mortality in heart failure patients

Machine learning to predict the clinical utility of biomedical research

A machine learning model to predict which scientific advances are likely to eventually translate to the clinic has been developed by Ian Hutchins and colleagues in the Office of Portfolio Analysis (OPA), a team led by George Santangelo at the National Institutes of Health (NIH). This work published in the journal PLOS Biology aims to… Continue reading Machine learning to predict the clinical utility of biomedical research

Machine learning algorithms to speed up image biomarker analysis in heart MRI scans

According to research published in journal Circulation:Cardiaovascular imaging cardiac MRI analysis utilizing machine learning algorithms can be performed significantly faster and with similar precision compared to human experts. In the study, researchers trained a neural network to read the cardiac MRI scans. Utilizing artificial intelligence, a scan can be analyzed in approximately four seconds compared… Continue reading Machine learning algorithms to speed up image biomarker analysis in heart MRI scans

Computational model to track flu using Twitter data

An international team led by Alessandro Vespignani from Northeastern University has developed a computational model to predict the spread of the flu in real time. This unique model uses posts on Twitter in combination with key parameters of each season’s epidemic, including the incubation period of the disease, the immunization rate, how many people an… Continue reading Computational model to track flu using Twitter data

Artificial intelligence project to identify skin cancer based on machine learning algorithm works as good as a doctor

Universal access to health care was on the minds of computer scientists at Stanford when they set out to create an artificially intelligent diagnosis algorithm for skin cancer. They made a database of nearly 130,000 skin disease images and trained their algorithm to visually diagnose potential cancer. From the very first test, it performed with… Continue reading Artificial intelligence project to identify skin cancer based on machine learning algorithm works as good as a doctor

Collaboration between UCSF, Intel to develop deep learning analytics for healthcare

UC San Francisco’s Center for Digital Health Innovation (CDHI) today announced a collaboration with Intel Corporation to deploy and validate a deep learning analytics platform designed to improve care by helping clinicians make better treatment decisions, predict patient outcomes, and respond more nimbly in acute situations. The collaboration brings together Intel’s leading-edge computer science and… Continue reading Collaboration between UCSF, Intel to develop deep learning analytics for healthcare

Dutch universities collaborate on big data in health to understand disease process

Patients with the same illness often receive the same treatment, even if the cause of the illness is different for each person. This represents a new step towards ultimately being able to offer every patient more personalized treatment. Six Dutch universities are combining forces to chart the different disease processes for a range of common… Continue reading Dutch universities collaborate on big data in health to understand disease process

Dynamic undocking, a new computational method for efficient drug research

Researchers of the University of Barcelona have developed a more efficient computational method to identify new drugs. The study, published in the scientific journal Nature Chemistry, proposes a new way of facing the discovery of molecules with biological activity.Researchers devised dynamic undocking (DUck), a fast computational method to calculate the work necessary to reach a… Continue reading Dynamic undocking, a new computational method for efficient drug research