Abstract
ICU mortality risk prediction is a tough yet important task. On one hand, due to the complex temporal data collected, it is difficult to identify the effective features and interpret them easily; on the other hand, good prediction can help clinicians take timely actions to prevent the mortality. These correspond to the interpretability and accuracy problems. Most existing methods lack of the interpretability, but recently Sub-graph Augmented Nonnegative Matrix Factorization (SANMF) has been successfully applied to time series data to provide a path to interpret the features well. Therefore, we adopted this approach as the backbone to analyze the patient data. One limitation of the original SANMF method is its poor prediction ability due to its unsupervised nature. To deal with this problem, we proposed a supervised SANMF algorithm by integrating the logistic regression loss function into the NMF framework and solved it with an alternating optimization procedure. We used the simulation data to verify the effectiveness of this method, and then we applied it to ICU mortality risk prediction and demonstrated its superiority over other conventional supervised NMF methods.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 |
| Editors | Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1189-1194 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538654880 |
| DOIs | |
| State | Published - Jan 21 2019 |
| Event | 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain Duration: Dec 3 2018 → Dec 6 2018 |
Publication series
| Name | Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 |
|---|
Conference
| Conference | 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 |
|---|---|
| Country | Spain |
| City | Madrid |
| Period | 12/3/18 → 12/6/18 |
Fingerprint
Keywords
- ICU mortality risk
- Logistic regression
- Nonnegative matrix factorization
- Representation
- Supervised learning
ASJC Scopus subject areas
- Biomedical Engineering
- Health Informatics
Cite this
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Supervised Nonnegative Matrix Factorization to Predict ICU Mortality Risk. / Chao, Guoqing; Mao, Chengsheng; Wang, Fei; Zhao, Yuan; Luo, Yuan.
Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1189-1194 8621403 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Supervised Nonnegative Matrix Factorization to Predict ICU Mortality Risk
AU - Chao, Guoqing
AU - Mao, Chengsheng
AU - Wang, Fei
AU - Zhao, Yuan
AU - Luo, Yuan
PY - 2019/1/21
Y1 - 2019/1/21
N2 - ICU mortality risk prediction is a tough yet important task. On one hand, due to the complex temporal data collected, it is difficult to identify the effective features and interpret them easily; on the other hand, good prediction can help clinicians take timely actions to prevent the mortality. These correspond to the interpretability and accuracy problems. Most existing methods lack of the interpretability, but recently Sub-graph Augmented Nonnegative Matrix Factorization (SANMF) has been successfully applied to time series data to provide a path to interpret the features well. Therefore, we adopted this approach as the backbone to analyze the patient data. One limitation of the original SANMF method is its poor prediction ability due to its unsupervised nature. To deal with this problem, we proposed a supervised SANMF algorithm by integrating the logistic regression loss function into the NMF framework and solved it with an alternating optimization procedure. We used the simulation data to verify the effectiveness of this method, and then we applied it to ICU mortality risk prediction and demonstrated its superiority over other conventional supervised NMF methods.
AB - ICU mortality risk prediction is a tough yet important task. On one hand, due to the complex temporal data collected, it is difficult to identify the effective features and interpret them easily; on the other hand, good prediction can help clinicians take timely actions to prevent the mortality. These correspond to the interpretability and accuracy problems. Most existing methods lack of the interpretability, but recently Sub-graph Augmented Nonnegative Matrix Factorization (SANMF) has been successfully applied to time series data to provide a path to interpret the features well. Therefore, we adopted this approach as the backbone to analyze the patient data. One limitation of the original SANMF method is its poor prediction ability due to its unsupervised nature. To deal with this problem, we proposed a supervised SANMF algorithm by integrating the logistic regression loss function into the NMF framework and solved it with an alternating optimization procedure. We used the simulation data to verify the effectiveness of this method, and then we applied it to ICU mortality risk prediction and demonstrated its superiority over other conventional supervised NMF methods.
KW - ICU mortality risk
KW - Logistic regression
KW - Nonnegative matrix factorization
KW - Representation
KW - Supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85062567370&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062567370&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2018.8621403
DO - 10.1109/BIBM.2018.8621403
M3 - Conference contribution
T3 - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
SP - 1189
EP - 1194
BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
A2 - Schmidt, Harald
A2 - Griol, David
A2 - Wang, Haiying
A2 - Baumbach, Jan
A2 - Zheng, Huiru
A2 - Callejas, Zoraida
A2 - Hu, Xiaohua
A2 - Dickerson, Julie
A2 - Zhang, Le
PB - Institute of Electrical and Electronics Engineers Inc.
ER -