Data covering the entire population of Denmark was used to train an AI to predict people's life outcomes An artificial intelligence trained on personal data covering the entire population of Denmark can predict people's chances of dying more accurately than any existing model, even those used in the insurance industry. The researchers behind the technology say it could also have a positive impact in early prediction of social and health problems – but must be kept out of the hands of big business. Sune Lehmann Jørgensen at the Technical University of Denmark and his colleagues used a rich dataset from Denmark that covers education, visits to doctors and hospitals, any resulting diagnoses, income and occupation for 6 million people from 2008 to 2020. They converted this dataset into words that could be used to train a large language model, the same technology that powers AI apps such as ChatGPT. These models work by looking at a series of words and determining which word is statistically most likely to come next, based on vast amounts of examples. In a similar way, the researchers' Life2vec model can look at a series of life events that form a person's history and determine what is most likely to happen next.

AstraZeneca's Dave Fredrickson discusses how the COVID-19 pandemic helped to bolster early cancer diagnosis from lung scans. Scientists have found that artificial intelligence could be an effective tool in predicting pancreatic cancer before a single symptom appears, according to a study published in the journal Nature Medicine on May 8. A team of researchers led by Copenhagen University Hospital in Denmark and Harvard Medical School in Boston completed a sweeping study to determine whether AI could flag a person's risk of developing the disease. The results exceeded their expectations, with the model successfully predicting risk up to three years before diagnosis. In 2023, about 64,050 people in the U.S. will be diagnosed with pancreatic cancer and about 50,550 will die from the aggressive disease, the American Cancer Society (ACS) says.

Lung cancer is the leading cause of cancer death in the United States and around the world. Low-dose chest computed tomography (LDCT) is recommended to screen people between 50 and 80 years of age with a significant history of smoking, or who currently smoke. Lung cancer screening with LDCT has been shown to reduce death from lung cancer by up to 24 percent. But as rates of lung cancer climb among non-smokers, new strategies are needed to screen and accurately predict lung cancer risk across a wider population. A study led by investigators from the Mass General Cancer Center, a member of Mass General Brigham, in collaboration with researchers at the Massachusetts Institute of Technology (MIT), developed and tested an artificial intelligence tool known as Sybil.

Researchers from the Mayo Clinic have found that artificial intelligence can be used to help analyze if pregnant patients can have a safe delivery and avoid complications. The researchers examined more than 700 health factors in more than 66,000 deliveries, according to a recent study published in PLOS ONE. "Utilization of machine-learning–based algorithms may provide a dynamic, cumulative, and individualized model for prediction of outcomes of vaginal delivery and facilitation of intrapartum decision making," the authors wrote. Researchers said to their knowledge, this is the first time that researchers have attempted to apply machine learning algorithms to managing labor. The researchers described their work as the initial step, but a promising indicator, of the use in AI to help reduce pregnancy complications and maternal death.

BMJ Open. 2022 Apr 1;12(4):e053590. doi: 10.1136/bmjopen-2021-053590.ABSTRACTOBJECTIVES: To develop and validate tests to assess the risk of any …

A novel artificial intelligence score provides a more accurate forecast of the likelihood of patients with suspected or known coronary artery disease dying within 10 years than established scores used by health professionals worldwide. The research is presented today at EuroEcho 2021, a scientific congress of the European Society of Cardiology (ESC).[1] Unlike traditional methods based on clinical data, the new score also includes imaging information on the heart, measured by stress cardiovascular magnetic resonance (CMR). "Stress" refers to the fact that patients are given a drug to mimic the effect of exercise on the heart while in the magnetic resonance imaging scanner. "This is the first study to show that machine learning with clinical parameters plus stress CMR can very accurately predict the risk of death," said study author Dr. Theo Pezel of the Johns Hopkins Hospital, Baltimore, US. "The findings indicate that patients with chest pain, dyspnoea, or risk factors for cardiovascular disease should undergo a stress CMR exam and have their score calculated. This would enable us to provide more intense follow-up and advice on exercise, diet, and so on to those in greatest need."

A new machine learning system is better at predicting the likelihood of patients with cardiovascular problems dying within ten years than healthcare professionals' methods, according to a study presented at the EuroEcho 2021, a scientific meeting of the European Society Cardiology. Unlike traditional methods based solely on clinical data, the new machine learning system also includes results from imaging scans on the heart, measured by stress cardiovascular magnetic resonance (CMR). During this exam, patients receive a drug that mimics the effect of exercise on the heart and then undergo imaging using a magnetic resonance imaging scanner. Assessing the risk of death is commonly done in these patients. Usually, doctors use a limited amount of clinical information, including age, sex, smoking, blood pressure, and cholesterol levels.

Artificial intelligence could be used to predict who is at risk of developing type 2 diabetes--information that could be used to improve the lives of millions of Canadians. Researchers at the University of Toronto used a machine learning model to analyze health data, collected between 2006 to 2016, of 2.1 million people living in Ontario. They found that they were able to use the model to accurately predict the number of people who would develop type 2 diabetes within a five-year time period. The machine learning model was also able to analyze different factors that would influence whether people were high or low risk to develop the disease. The results of the study were recently published in the journal JAMA Network Open.

Now, Johns Hopkins Medicine researchers have developed an advanced machine-learning system that can accurately predict how a patient's bout with COVID-19 will go, and relay its findings back to the clinician in an easily understandable form. The new prognostic tool, known as the Severe COVID-19 Adaptive Risk Predictor (SCARP), can help define the one-day and seven-day risk of a patient hospitalized with COVID-19 developing a more severe form of the disease or dying from it. SCARP asks for a minimal amount of input to give an accurate prediction, making it fast, simple to use and reliable for basing treatment and care decisions. The new tool is described in a paper first posted online March 2 in the Annals of Internal Medicine. "SCARP was designed to provide clinicians with a predictive tool that is interactive and adaptive, enabling real-time clinical variables to be entered at a patient's bedside," says Matthew Robinson, M.D., assistant professor of medicine at the Johns Hopkins University School of Medicine and senior author of the paper.

The diagnosis of oral cancer could be'revolutionised' by using artificial intelligence to predict whether someone is likely to develop the disease, experts have said. Experts led from the Universities of Sheffield and Warwick have teamed up to investigate how machine learning could be applied to aid doctors in early detection. Diagnoses of oral cancers -- including those of the mouth, tongue and tonsils -- have increased by almost 60 per cent over the last decade, team noted. The risk of such cancers is heightened by such factors as alcohol consumption, increasing age, insufficient fruit and vegetables, tobacco and viral infection. Doctors evaluate the likelihood of pre-cancerous changes in the lining of the mouth -- so-called oral epithelial dysplasia -- developing into cancer using 15 criteria.