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Novel Machine Learning Approach to Identify Preoperative Risk Factors Associated With Super-Utilization of Medicare Expenditure Following Surgery
Using a machine learning algorithm, resource utilization among more than 1 million Medicare patients undergoing one of 6 operations including CABG and lung resection was analyzed. Super users comprised 4.8% of the cohort but consumed 31.7% of the resources. Risk factors for super use included paraplegia/hemiplegia, weight loss, and CHF combined with chronic kidney disease.