A Novel Technique for Identifying Patients with ICU Needs Using Hemodynamic Features
Identification of patients requiring intensive care is a critical issue in clinical treatment. The objective of this study is to develop a novel methodology using hemodynamic features for distinguishing such patients requiring intensive care from a group of healthy subjects. In this study, based on...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Advances in Fuzzy Systems |
| Online Access: | http://dx.doi.org/10.1155/2012/696194 |
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| author | A. Jalali P. Ghorbanian A. Ghaffari C. Nataraj |
| author_facet | A. Jalali P. Ghorbanian A. Ghaffari C. Nataraj |
| author_sort | A. Jalali |
| collection | DOAJ |
| description | Identification of patients requiring intensive care is a critical issue in clinical treatment. The objective of this study is to develop a novel methodology using hemodynamic features for distinguishing such patients requiring intensive care from a group of healthy subjects. In this study, based on the hemodynamic features, subjects are divided into three groups: healthy, risky and patient. For each of the healthy and patient subjects, the evaluated features are based on the analysis of existing differences between hemodynamic variables: Blood Pressure and Heart Rate. Further, four criteria from the hemodynamic variables are introduced: circle criterion, estimation error criterion, Poincare plot deviation, and autonomic response delay criterion. For each of these criteria, three fuzzy membership functions are defined to distinguish patients from healthy subjects. Furthermore, based on the evaluated criteria, a scoring method is developed. In this scoring method membership degree of each subject is evaluated for the three classifying groups. Then, for each subject, the cumulative sum of membership degree of all four criteria is calculated. Finally, a given subject is classified with the group which has the largest cumulative sum. In summary, the scoring method results in 86% sensitivity, 94.8% positive predictive accuracy and 82.2% total accuracy. |
| format | Article |
| id | doaj-art-be05286864c54910adbce0d20ee7b440 |
| institution | Kabale University |
| issn | 1687-7101 1687-711X |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Fuzzy Systems |
| spelling | doaj-art-be05286864c54910adbce0d20ee7b4402025-02-03T05:53:37ZengWileyAdvances in Fuzzy Systems1687-71011687-711X2012-01-01201210.1155/2012/696194696194A Novel Technique for Identifying Patients with ICU Needs Using Hemodynamic FeaturesA. Jalali0P. Ghorbanian1A. Ghaffari2C. Nataraj3Department of Mechanical Engineering, Villanova University, 800 Lancaster Avenue, Villanova, PA 19085, USADepartment of Mechanical Engineering, Villanova University, 800 Lancaster Avenue, Villanova, PA 19085, USADepartment of Mechanical Engineering, K.N.Toosi University of Technology, No. 19, Pardis street, Mollasadra Avenue, Vanak Square, Tehran 19991, IranDepartment of Mechanical Engineering, Villanova University, 800 Lancaster Avenue, Villanova, PA 19085, USAIdentification of patients requiring intensive care is a critical issue in clinical treatment. The objective of this study is to develop a novel methodology using hemodynamic features for distinguishing such patients requiring intensive care from a group of healthy subjects. In this study, based on the hemodynamic features, subjects are divided into three groups: healthy, risky and patient. For each of the healthy and patient subjects, the evaluated features are based on the analysis of existing differences between hemodynamic variables: Blood Pressure and Heart Rate. Further, four criteria from the hemodynamic variables are introduced: circle criterion, estimation error criterion, Poincare plot deviation, and autonomic response delay criterion. For each of these criteria, three fuzzy membership functions are defined to distinguish patients from healthy subjects. Furthermore, based on the evaluated criteria, a scoring method is developed. In this scoring method membership degree of each subject is evaluated for the three classifying groups. Then, for each subject, the cumulative sum of membership degree of all four criteria is calculated. Finally, a given subject is classified with the group which has the largest cumulative sum. In summary, the scoring method results in 86% sensitivity, 94.8% positive predictive accuracy and 82.2% total accuracy.http://dx.doi.org/10.1155/2012/696194 |
| spellingShingle | A. Jalali P. Ghorbanian A. Ghaffari C. Nataraj A Novel Technique for Identifying Patients with ICU Needs Using Hemodynamic Features Advances in Fuzzy Systems |
| title | A Novel Technique for Identifying Patients with ICU Needs Using Hemodynamic Features |
| title_full | A Novel Technique for Identifying Patients with ICU Needs Using Hemodynamic Features |
| title_fullStr | A Novel Technique for Identifying Patients with ICU Needs Using Hemodynamic Features |
| title_full_unstemmed | A Novel Technique for Identifying Patients with ICU Needs Using Hemodynamic Features |
| title_short | A Novel Technique for Identifying Patients with ICU Needs Using Hemodynamic Features |
| title_sort | novel technique for identifying patients with icu needs using hemodynamic features |
| url | http://dx.doi.org/10.1155/2012/696194 |
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