The GI Optimization project created a set of standard endoscopy procedure orders to replace the 80+ different ordering methods used by providers across Cleveland Clinic’s enterprise.
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.
Cleveland Clinic Cancer Center investigators have published a body of research that may herald an era of improved staging and risk stratification for patients with Merkel cell carcinoma, utilizing distinct histologic patterns in sentinel lymph node biopsies.
In the highly nuanced field of congenital heart surgery, care must be taken to level the playing field when comparing outcomes among centers, surgeons and patients. In this article, Tara Karamlou, MD, MSc, discusses the importance of risk stratification and her ultimate goal of being able to predict outcomes for individual patients.
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A novel biomarker derived via machine learning analysis of perivascular fat predicts cardiac risk better than current risk stratification methods, a new study finds.
These three genetic models could help refine recurrence risk and guide treatment decisions for patients with renal cell carcinoma.