Fuzzy risk stratification and risk assessment model for clinical monitoring in the ICU
| Autor: | Albion Dervishi |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
medicine.medical_specialty
Health Informatics computer.software_genre Fuzzy logic Risk Assessment 03 medical and health sciences 0302 clinical medicine Fuzzy Logic Intensive care Medicine Humans 030212 general & internal medicine Cluster analysis Monitoring Physiologic Receiver operating characteristic business.industry Continuous monitoring 030208 emergency & critical care medicine Computer Science Applications Random forest Support vector machine Intensive Care Units Emergency medicine Data mining business Risk assessment computer |
| Zdroj: | Computers in biology and medicine. 87 |
| ISSN: | 1879-0534 |
| Popis: | Background The decisions that clinicians make in intensive care units (ICUs) based on monitored parameters reflecting physiological deterioration are of major medical and biomedical engineering interest. These parameters have been investigated and assessed for their usefulness in risk assessment. Methods Totally, 127 ICU adult patients were studied. They were selected from a MIMIC II Waveform Database Matched Subset and had continuous monitoring of heart rate, invasive blood pressure, and oxygen saturation. The monitored data were dimension reduced using deep learning autoencoders and then used to train a support vector machine model (SVM). A combination of methods including fuzzy c-means clustering (FCM), and a random forest (RF) was used to determine the risk levels. Results When classifying patients into stable or deteriorating groups the main performance parameter was the receiver operating characteristics (ROC). The area under the ROC (AUROC) was 93.2 (95% CI (92.9–93.4)) with sensitivity and specificity values of 0.80 and 0.89, respectively. The suggested fuzzy risk levels using the combined method of the FCM clustering and RF achieved an accuracy of 1 (0.9999, 1), with both sensitivity and specificity values equal to 1. Conclusions The potential for using models in risk assessment to estimate a patient's physiological status, stable or deteriorating, within 4 h has been demonstrated. The study was based on retrospective analysis and further studies are needed to evaluate the impact on clinical outcomes using this model. |
| Databáze: | OpenAIRE |
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