It can be difficult for clinicians and patients to predict when an acute asthma exacerbation might occur. Machine learning can help change that, according to one new Cleveland Clinic study.
Cleveland Clinic is collaborating with the NFL Players Association to create a research network that uses machine learning to develop tools to help identify, treat and prevent brain disorders.
For the first time, a large-scale machine learning-based approach has demonstrated accurate and generalizable assessment of patients’ risk for cancer therapy-related cardiac dysfunction.
Advanced machine learning techniques will enable future research efforts aimed at discovering complex phenotype-genotype correlations and predicting population-level outcomes not just in AML, but in other fields of medical research as well.
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Researchers have developed the first machine-learning model that can predict with high accuracy MDS patient response or resistance to HMA treatment.
When a patient outsmarts traditional cognitive screening tests, an AI-based test can catch memory impairment.
We’re involved with NIH-funded multicenter projects deploying computer algorithms and machine learning to better predict cardiac allograft rejection and improve characterization of a rare cardiomyopathy.
A recent study coauthored by Cleveland Clinic’s Ardeshir Hashmi, MD, explored the interaction of subjective and objective cognitive screening tools for patients over age 65.
Learn more about recent advances in machine learning and hematological malignancies in this review coauthored by the Director of the Center for Clinical Artificial Intelligence
One of our newest cardiac surgeons shares how he found his surgical passion and how he’s connecting it with doctoral studies in machine learning to try to enhance risk prediction for his patients.