Abstract
Abstract Tight glycaemic control (TGC) is a treatment in the intensive care in order to avoid stress-induced hyperglycaemia. The insulin sensitivity (SI) prediction is an essential step of the best performing, clinically applied so-called STAR (Stochastic-TARgeted) TGC protocol. Previous results showed performance improvement of the SI prediction using artificial intelligence methods. This study analyses the clinical performance of distinct artificial intelligence based SI prediction methods (2 different neural network based prediction methods: Classification Deep Network and Mixture Density Network with 3 different parametrizations and 2 variants: sex-specific and non sex-specific for each). In-silico validation was used for evaluation simulating the treatment of 171 virtual patients. Based on the results the number of input parameters involved into the prediction can effectively increase the reliability of the SI prediction. Improvements in the performance are also experienced in several cases by using sex-specific models.
Published Version
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