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.
New Computer Model Helps Predict Outcomes for Individual Acute Myeloid Leukemia 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.
Researchers Unlock Keys to Staging and Risk Stratification of Merkel Cell Carcinoma
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.
Congenital Heart Care: Performance Assessment Among Institutions and Surgeons
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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Machine Learning Analysis of Perivascular Fat on Coronary CT Improves Cardiac Risk Prediction
A novel biomarker derived via machine learning analysis of perivascular fat predicts cardiac risk better than current risk stratification methods, a new study finds.
Genetic Models for Renal Cell Carcinoma Risk Stratification: Current Status and Future Prospects
These three genetic models could help refine recurrence risk and guide treatment decisions for patients with renal cell carcinoma.