CMS has announced ClosedLoop.ai as the winner and Geisinger as the runner-up in its Artificial Intelligence Health Outcomes Challenge, a multi-stage competition that began in 2019.
The AI Health...
Suicide risk prediction models that perform well in the general population may not be as accurate for Black, American Indian, and Alaska Native people, potentially worsening ethnic and racial...
Researchers from Michigan Medicine have developed a predictive analytics model that can accurately identify patient deterioration for both general ward and COVID-19 patients.
The algorithm was more...
Researchers from Michigan Medicine have developed a predictive analytics tool that can help identify pulmonary arterial hypertension, a condition that causes blocked or destroyed blood vessels in the...
NorthShore University HealthSystem has launched a system-wide genomics program that will leverage predictive analytics to improve care for hereditary cancer, cardiovascular diseases, and rare...
Researchers at MIT’s Data to AI Lab (DAI Lab) have developed a new framework that can streamline machine learning processes to help organizations uncover actionable insights from big data.
The...
In healthcare, predictive analytics tools have always been one of the most promising applications of big data. The ability to anticipate negative consequences before they occur is a major asset for any...
The National Minority Quality Forum (NMQF) has launched its COVID-19 Index, a predictive analytics tool that will help leaders prepare for future surges of coronavirus.
The COVID-19 Index will enable...
At first glance, mining clinical data to extract meaningful insights seems like a task well-suited for machine learning algorithms. The principal aim of advanced analytics tools is to evaluate multiple...
Statistical suicide risk prediction models could be implemented cost-effectively in healthcare organizations and may help save many lives each year, according to a study published in JAMA...
A machine learning tool can analyze EHR data to calculate suicide attempt risk and help providers know which patients to screen in nonpsychiatric clinical settings, a study published in JAMA Network...
When applied to a clinical data registry, machine learning algorithms showed no significant improvement in predicting adverse outcomes after an acute myocardial infarction (AMI), a study published in...
Using a new data analytics approach, researchers could enhance cancer drug response predictions by accounting for overlooked variation across and within cancer types, a study published in PLOS...
Researchers at Thomas Jefferson University have developed a predictive analytics tool that can forecast the risk of post-surgical complications, helping providers to deliver more proactive, preventive...
A deep learning tool was able to accurately predict survival expectancy in patients with lung cancer, potentially leading to more informed care decisions by providers, according to a study published in...
Of the many truths brought to light during the pandemic, the importance of providers’ voices is among the most consequential.
The experiences and daily operations of frontline healthcare workers...
Artificial intelligence could help identify factors in EHR data that are predictive of poor COVID-19 outcomes and mortality, according to a study published in npj Digital Medicine.
The vast amount of...
In hopsitals and emergency departments, it’s essential for providers to continually monitor patients in order to stay ahead of potential adverse events. However, patient deterioration can be very...
Using predictive analytics algorithms, a universal screening tool can accurately determine an adolescent’s suicide risk and alert providers of which patients are in need of follow-up...
A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed a predictive analytics model that can jointly model a patient’s breast cancer risk...
