Researchers at New York Eye and Ear Infirmary of Mount Sinai (NYEE) have developed deep learning tools that can detect age-related macular degeneration (AMD), a leading cause of blindness in the...
Certain risk scores already used to evaluate the severity of a trauma patient’s condition can also help providers determine which patients are highly likely to develop multiple infections,...
A predictive analytics model was able to accurately identify patients at higher than normal risk for pancreatic cancer, according to a study published in Cancer Epidemiology, Biomarkers &...
In medicine, risk scoring is one of the best methods providers can use to anticipate and prepare for an adverse event. Using patient data and analytics tools, clinicians can determine the likelihood...
As the COVID-19 pandemic continues to disrupt the status quo, the healthcare industry is turning to real-world data to better understand, monitor, and prepare for whatever the virus may bring.
For...
Researchers from Stanford University’s department of surgery have developed a predictive model that provides best practice guidelines for operating room team members during the COVID-19 pandemic...
Using artificial intelligence, NYU researchers accurately predicted which patients newly diagnosed with COVID-19 would go on to develop severe respiratory disease, according to a study published in...
Mount Sinai researchers have developed new predictive analytics tools and identified environmental risk factors that could lead to a new understanding of what triggers Crohn’s disease.
In a...
An individual’s social determinants of health can provide valuable information about a person’s potential risk of heart disease, but these non-clinical factors are often overlooked,...
Cardiovascular factors typically evaluated in an annual physical, including blood pressure, cholesterol levels, diabetes, and smoking status, are just as accurate in predicting who will develop...
A simple predictive risk model helped forecast stroke risk in adult patients who have migraine with aura, according to research funded by the National Institute on Minority Health and Health...
If the evolution of the healthcare industry were a jigsaw puzzle, then predictive analytics would be the last pieces put into place.
For years, experts have talked about the potential for artificial intelligence, machine learning,...
Researchers at the University of Tennessee, Knoxville, have developed a predictive analytics algorithm that can identify future at-risk patients who have undergone hernia surgery, helping providers to...
High levels of soluble urokinase plasminogen activator receptor (suPAR), a protein produced by immune cells in the bone marrow, could inform risk prediction and prevention of acute kidney injury (AKI),...
A team from the Massachusetts Institute of Technology (MIT) has developed a method that determines the accuracy of predictive risk models, helping clinicians to choose better treatments for their...
A DNA sequencing technique analyzes tumors to accurately determine which melanoma patients are at risk for cancer recurrence and spread, according to a study published in Nature Cancer.
In most...
A machine learning algorithm accurately predicted inpatient and emergency department (ED) utilization using only publicly available social determinants of health (SDOH) data, showing that it’s...
Children enrolled in Medicaid who are exposed to a population health management program could experience 50 fewer hospital admissions each month and 3,600 fewer bed-days in a year, according to a study...
The use of machine learning, natural language processing, and neural networking artificial intelligence technologies are exploding across the healthcare industry. From identifying early stage cancer on...
Machine learning tools could predict patients’ long-term risk of heart attacks and cardiac deaths better than standard methods used by cardiologists, according to a study published in...
