420 research outputs found
Long-term results in pancreatic transplantation with special emphasis on the use of prolamine
Our pancreatic transplantation programme was initiated in 1979. Since then a total of 102 pancreas transplantations have been performed, blocking exocrine secretion using the duct occlusion technique with prolamine. Early non-immunological complications are frequent. The long-term results (9 years) in combined pancreas and kidney transplanted patients are satisfying: the survival rate for pancreas is 38% and 54% for kidney. Patient survival rate in this period is 85%. Beyond the first year post-transplant the exocrine activity disappears whereas the endocrine function remains well preserved
Poster: Rallying the Team to Improve Glycemic Control in the Acute Care Setting: Mission Possible
A Novel Tropical Geometry-based Interpretable Machine Learning Method: Pilot Application to Delivery of Advanced Heart Failure Therapies
Abstract—A model’s interpretability is essential to many practical applications such as clinical decision support systems. In this paper, a novel interpretable machine learning method is presented, which can model the relationship between input variables and responses in humanly understandable rules. The method is built by applying tropical geometry to fuzzy inference systems, wherein variable encoding functions and salient rules can be discovered by supervised learning. Experiments using synthetic datasets were conducted to demonstrate the performance and capacity of the proposed algorithm in classification and rule discovery. Furthermore, we present a pilot application in identifying heart failure patients that are eligible for advanced therapies as proof of principle. From our results on this particular application, the proposed network achieves the highest F1 score. The network is capable of learning rules that can be interpreted and used by clinical providers. In addition, existing fuzzy domain knowledge can be easily transferred into the network and facilitate model training. In our application, with the existing knowledge, the F1 score was improved by over 5%. The characteristics of the proposed network make it promising in applications requiring model reliability and justification
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