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Taking race into account when developing tools to predict a patient’s risk of colorectal cancer leads to more accurate predictions when compared with race-blind algorithms, researchers find.
While many medical researchers have argued that race should be removed as a factor from clinical algorithms that predict disease risks, a new study finds that, at least for colorectal cancer, including race can help correct a data issue – inaccurate recording of family history for Black patients.
The U.S. National Science Foundation (NSF) has awarded $12 million to a multi-institutional team of researchers that includes Cornell Tech Assistant Professor Udit Gupta for an initiative to establish new standards for carbon accounting in the computing industry.
A Cornell-led collaboration developed machine-learning models that use these cell-free molecular RNA dregs to diagnose pediatric inflammatory conditions that are difficult to differentiate. The diagnostic tool can accurately determine if a patient has Kawasaki disease (KD), Multisystem Inflammatory Syndrome in Children (MIS-C), a viral infection or a bacterial infection, while simultaneously monitoring the patient’s organ health.
Integrating artificial intelligence into today’s environmental control systems could reduce energy consumption for indoor agriculture by 25% – potentially helping to feed the world as its population rises, Cornell engineers have found.
Researchers have uncovered DNA markers associated with retroelements, remnants of ancient viral genetic material, in our genes that act as highly accurate epigenetic clocks predicting chronological age.
Researchers at Weill Cornell Medicine and NewYork-Presbyterian found that a self-guided cognitive behavioral therapy app significantly reduced anxiety in young adults struggling with mental health challenges.
Cornell Engineering’s Scientific Artificial Intelligence Center has partnered with Pasteur Labs, an alumnus-founded startup, to establish new research projects in human-AI collaboration for scientific discovery and industrial applications.
Researchers at Weill Cornell Medicine have used machine learning to define three subtypes of Parkinson’s disease based on the pace at which the disease progresses.
