This paper studies the problem of real-time monitoring, diagnosing, and predicting multiple outcomes of anesthesia patients. Anesthesia drugs have impact on multiple outcomes of an anesthesia patient. Most typical outcomes include anesthesia depth, blood pressures, heart rates, etc. Traditional diagnosis and control in anesthesia focus on a one-drug-one-outcome scenario. Our results show that consideration of multiple outcomes is necessary and beneficial for anesthesia managements. Due to limited real-time data and complexities in patient modeling, the task of real-time modeling in multi-outcome modeling is of substantial challenge. This paper introduces a direct method of prediction and control oriented modeling that significantly reduces the complexity of the problem. Clinical data are used to evaluate the effectiveness of the method. Based on the multi-outcome model, patient response prediction, drug impact prediction and multiple outputs management can be made to effectively administrate the anesthetics and avoid unexpected dangerous states of the patient undergoing anesthesia.
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