The safety of critically ill patients in intensive care units is an important aspect of medical care. Many human factors contribute to deficiencies and errors in patient care in the intensive care setting, such as long working hours, high levels of stress, lack of enough people, may cause human errors and affecting the effectiveness of the decisions of the physician. Several attempts have been made to increase the effectiveness of such decisions by issuing early alerts on adverse patient conditions. However, such alerts are based on single parameter variations, and not on the relationship between multiple parameter variations. We developed a computerbased model is an integrated solution which identifies adverse patient events based on multiple parameter variations, and then provides predictive treatment suggestions based on the likely clinical conditions which result in the parameter variations. The proposed system follows an interactive communication cycle in order to properly notify the responsible treating physicians at different tiers of responsibility. Our model is capable of early identification of adverse conditions and providing suitable treatment suggestions, thus acting as a decision support system to assist the treating physician.