Abstract
This research proposes a potential organ donor prediction model using an artificial neural network-based approach to forecast the amount of daily incoming organ referrals and their medical suitability. The daily amount of incoming organ referrals and their medical suitability indicate organ donation potential. Predicting organ donation potential is vital for organ procurement organizations (OPOs) to improve staffing and scheduling practices. As a result, the objective of this study is to develop an accurate organ donation potential prediction model that can help the OPOs in achieving their mission to save lives. Several supervised artificial neural networks were designed, tested and compared with each other to identify best prediction accuracy. The experimental results and analyses indicate that the prediction accuracy for organ donation potential depends not only on the network type, but also on the architecture selection and the choice of inputs and time frame. The constructed neural network model shows good organ potential prediction accuracy with a R-squared value of 0.73. Hence, implementing such a model can support organ procurement organizations in their mission to save lives.
| Original language | English (US) |
|---|---|
| Title of host publication | IIE Annual Conference and Expo 2013 |
| Publisher | Institute of Industrial Engineers |
| Pages | 1532-1541 |
| Number of pages | 10 |
| State | Published - 2013 |
| Externally published | Yes |
| Event | IIE Annual Conference and Expo 2013 - San Juan, Puerto Rico Duration: May 18 2013 → May 22 2013 |
Other
| Other | IIE Annual Conference and Expo 2013 |
|---|---|
| Country | Puerto Rico |
| City | San Juan |
| Period | 5/18/13 → 5/22/13 |
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Keywords
- Artificial neural networks
- Organ donation
- Organ referral prediction
- Time series forecasting
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
Cite this
A neural network-based approach for predicting organ donation potential. / Schleich, Benjamin R.; Lam, Sarah S.; Yoon, Sang Won; Tajik, Waheed; Goldstein, Michael; Irving, Helen.
IIE Annual Conference and Expo 2013. Institute of Industrial Engineers, 2013. p. 1532-1541.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A neural network-based approach for predicting organ donation potential
AU - Schleich, Benjamin R.
AU - Lam, Sarah S.
AU - Yoon, Sang Won
AU - Tajik, Waheed
AU - Goldstein, Michael
AU - Irving, Helen
PY - 2013
Y1 - 2013
N2 - This research proposes a potential organ donor prediction model using an artificial neural network-based approach to forecast the amount of daily incoming organ referrals and their medical suitability. The daily amount of incoming organ referrals and their medical suitability indicate organ donation potential. Predicting organ donation potential is vital for organ procurement organizations (OPOs) to improve staffing and scheduling practices. As a result, the objective of this study is to develop an accurate organ donation potential prediction model that can help the OPOs in achieving their mission to save lives. Several supervised artificial neural networks were designed, tested and compared with each other to identify best prediction accuracy. The experimental results and analyses indicate that the prediction accuracy for organ donation potential depends not only on the network type, but also on the architecture selection and the choice of inputs and time frame. The constructed neural network model shows good organ potential prediction accuracy with a R-squared value of 0.73. Hence, implementing such a model can support organ procurement organizations in their mission to save lives.
AB - This research proposes a potential organ donor prediction model using an artificial neural network-based approach to forecast the amount of daily incoming organ referrals and their medical suitability. The daily amount of incoming organ referrals and their medical suitability indicate organ donation potential. Predicting organ donation potential is vital for organ procurement organizations (OPOs) to improve staffing and scheduling practices. As a result, the objective of this study is to develop an accurate organ donation potential prediction model that can help the OPOs in achieving their mission to save lives. Several supervised artificial neural networks were designed, tested and compared with each other to identify best prediction accuracy. The experimental results and analyses indicate that the prediction accuracy for organ donation potential depends not only on the network type, but also on the architecture selection and the choice of inputs and time frame. The constructed neural network model shows good organ potential prediction accuracy with a R-squared value of 0.73. Hence, implementing such a model can support organ procurement organizations in their mission to save lives.
KW - Artificial neural networks
KW - Organ donation
KW - Organ referral prediction
KW - Time series forecasting
UR - http://www.scopus.com/inward/record.url?scp=84900340315&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84900340315&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84900340315
SP - 1532
EP - 1541
BT - IIE Annual Conference and Expo 2013
PB - Institute of Industrial Engineers
ER -