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.
A computer model developed by Cleveland Clinic Cancer Center researchers and their colleagues who applied machine learning to clinical and genomic datasets is able to predict the survival of individual acute myeloid leukemia patients more accurately than existing models.
Opportunities are on the horizon for AI and nursing practice. Associate Chief Nursing Officer Nelita Iuppa, PhD, RN, shares how AI is partnering with nursing to provide patient care.
The field of diabetes management is seeing rapid innovation. Find out how your patients may benefit from sophisticated risk prediction modeling and machine learning systems.
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One driver of increased pharmaceutical spending is the high failure rate of expensive and time-consuming randomized control trials. deepDTnet, a network-based deep learning methodology for novel target identification and in silico drug repurposing, may aid in the development of novel, effective treatment strategies for complex diseases.
The secret to making healthcare data an asset rather than a burden lies in ensuring that it’s well-characterized data from real-world practice. We share top-level insights from our experience, including the centrality of collaboration.
We’ve developed an artificial intelligence system to automatically detect seizures from long-term EEG recordings in an epilepsy monitoring unit. Strong performance in early datasets hints at big potential implications for epilepsy diagnosis and care.
Progress toward use of machine learning and deep learning techniques to inform epilepsy surgery decisions for individual patients is well underway. We share a status report on Cleveland Clinic’s experience in this space.
Differentiating true glioblastoma progression from pseudoprogression is a vexing challenge with big implications for management. New research reveals the promise of taking an algorithmic approach to MRI evaluation.
A novel biomarker derived via machine learning analysis of perivascular fat predicts cardiac risk better than current risk stratification methods, a new study finds.