Abstract
In this paper we present a new expert knowledge based clinical decision support system for prediction of intensive care units outcome based on the physiological measurements collected during the first 48 hours of the patient's admission to the ICU. The developed CDSS algorithm is composed of several stages. First, we categorize the collected data based on the physiological organ that they represent. We then extract clinically relevant features from each data category and then rank these features based on their mutual information with the outcome. Then, we design an artificial neural network to serve as a classifier to detect patients at high risk of critical deterioration. We use the eight-fold cross validation method to test the developed CDSS classifier. The results from the classification show that the newly designed CDSS outperforms the widely used acuity scoring systems, SOFA and SAPS-III. The F-score classification result of our developed algorithms is 42% while the F-score results for SOFA and SAPS-III are 26% and 29% respectively.
| Original language | English (US) |
|---|---|
| Title of host publication | 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2520-2524 |
| Number of pages | 5 |
| Volume | 2016-October |
| ISBN (Electronic) | 9781457702204 |
| DOIs | |
| State | Published - Oct 13 2016 |
| Externally published | Yes |
| Event | 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States Duration: Aug 16 2016 → Aug 20 2016 |
Other
| Other | 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 |
|---|---|
| Country | United States |
| City | Orlando |
| Period | 8/16/16 → 8/20/16 |
Fingerprint
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics
Cite this
Advanced analytics for outcome prediction in intensive care units. / Jalali, Ali; Bender, Dieter; Rehman, Mohamed; Nadkanri, Vinay; Nataraj, C.
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Vol. 2016-October Institute of Electrical and Electronics Engineers Inc., 2016. p. 2520-2524 7591243.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Advanced analytics for outcome prediction in intensive care units
AU - Jalali, Ali
AU - Bender, Dieter
AU - Rehman, Mohamed
AU - Nadkanri, Vinay
AU - Nataraj, C.
PY - 2016/10/13
Y1 - 2016/10/13
N2 - In this paper we present a new expert knowledge based clinical decision support system for prediction of intensive care units outcome based on the physiological measurements collected during the first 48 hours of the patient's admission to the ICU. The developed CDSS algorithm is composed of several stages. First, we categorize the collected data based on the physiological organ that they represent. We then extract clinically relevant features from each data category and then rank these features based on their mutual information with the outcome. Then, we design an artificial neural network to serve as a classifier to detect patients at high risk of critical deterioration. We use the eight-fold cross validation method to test the developed CDSS classifier. The results from the classification show that the newly designed CDSS outperforms the widely used acuity scoring systems, SOFA and SAPS-III. The F-score classification result of our developed algorithms is 42% while the F-score results for SOFA and SAPS-III are 26% and 29% respectively.
AB - In this paper we present a new expert knowledge based clinical decision support system for prediction of intensive care units outcome based on the physiological measurements collected during the first 48 hours of the patient's admission to the ICU. The developed CDSS algorithm is composed of several stages. First, we categorize the collected data based on the physiological organ that they represent. We then extract clinically relevant features from each data category and then rank these features based on their mutual information with the outcome. Then, we design an artificial neural network to serve as a classifier to detect patients at high risk of critical deterioration. We use the eight-fold cross validation method to test the developed CDSS classifier. The results from the classification show that the newly designed CDSS outperforms the widely used acuity scoring systems, SOFA and SAPS-III. The F-score classification result of our developed algorithms is 42% while the F-score results for SOFA and SAPS-III are 26% and 29% respectively.
UR - http://www.scopus.com/inward/record.url?scp=85009133722&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85009133722&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2016.7591243
DO - 10.1109/EMBC.2016.7591243
M3 - Conference contribution
C2 - 28268836
AN - SCOPUS:85009133722
VL - 2016-October
SP - 2520
EP - 2524
BT - 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -