Metaheuristics for Discovering Favourable Continuous Intravenous Insulin Rate Protocols from Historical Patient Data
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Metaheuristic search algorithms such as particle swarm optimisation algorithm and covariance matrix adaptation evolution strategy are used to discover improved strategies for setting intravenous insulin rates of hospital in-patients with diabetes. We describe an approach combining and extending two existing methods recently reported in the literature: the Glucose Regulation for Intensive Care Patients (GRIP) method, and a favourability metric used for comparing competing strategies using historical medical records. We demonstrate with a dataset of blood glucose level/insulin infusion rate time series records from sixteen patients that new and significantly better insulin infusion strategies than GRIP can be discovered from this data.
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