Tan Zhibin, Kaddoum Romeo, Wang Le Yi, Wang Hong
Dept. of Electrical and Computer Engineering, Wayne State University, Detroit, Michigan 48202, USA.
Open Biomed Eng J. 2010;4:113-22. doi: 10.2174/1874120701004010113. Epub 2010 Jul 9.
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. This paper studies the problem of real-time modeling for monitoring, diagnosing, and predicting multiple outcomes of anesthesia patients. It is shown that consideration of multiple outcomes is necessary and beneficial for anesthesia managements. Due to limited real-time data, real-time modeling in multi-outcome modeling requires low-complexity model strucrtures. This paper introduces a method of decision-oriented modeling that significantly reduces the complexity of the problem. The method employs simplified and combined model functions in a Wiener structure to contain model complexity. The ideas of drug impact prediction and reachable sets are introduced for utility of the models in diagnosis, outcome prediction, and decision assistance. Clinical data are used to evaluate the effectiveness of the method.
麻醉药物会对麻醉患者的多种结果产生影响。最典型的结果包括麻醉深度、血压、心率等。传统的麻醉诊断与控制聚焦于单一药物对应单一结果的情况。本文研究麻醉患者多种结果的实时建模问题，用于监测、诊断和预测。结果表明，考虑多种结果对于麻醉管理是必要且有益的。由于实时数据有限，多结果建模中的实时建模需要低复杂度的模型结构。本文介绍了一种面向决策的建模方法，该方法显著降低了问题的复杂度。该方法在维纳结构中采用简化和组合的模型函数来控制模型复杂度。引入药物影响预测和可达集的概念，以用于模型在诊断、结果预测和决策辅助中的应用。利用临床数据评估该方法的有效性。
