Identification of patients requiring intensive care is a critical issue in clinical treatment. The objective of this study is to develop a\r\nnovel methodology using hemodynamic features for distinguishing such patients requiring intensive care from a group of healthy\r\nsubjects. In this study, based on the hemodynamic features, subjects are divided into three groups: healthy, risky and patient.\r\nFor each of the healthy and patient subjects, the evaluated features are based on the analysis of existing differences between\r\nhemodynamic variables: Blood Pressure and Heart Rate. Further, four criteria from the hemodynamic variables are introduced:\r\ncircle criterion, estimation error criterion, Poincare plot deviation, and autonomic response delay criterion. For each of these\r\ncriteria, three fuzzy membership functions are defined to distinguish patients from healthy subjects. Furthermore, based on the\r\nevaluated criteria, a scoring method is developed. In this scoring method membership degree of each subject is evaluated for the\r\nthree classifying groups. Then, for each subject, the cumulative sum of membership degree of all four criteria is calculated. Finally,\r\na given subject is classified with the group which has the largest cumulative sum. In summary, the scoring method results in 86%\r\nsensitivity, 94.8% positive predictive accuracy and 82.2% total accuracy.
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