Abstract
Heart failure (HF) has a highly variable annual mortality rate and there is an urgent need of determining patient prognosis to enable informed decision-making about heart failure treatment strategies. Existing survival risk prediction models either require features that limit their applicability or pose difficulties for parameter estimation as physicians have to use a limited set of variables with known hazard ratios published in literature. We propose a new model to predict the probabilistic survival score after HF diagnosis based on all clinical variables derived from the electronic health record (EHR). We formalize the parameter estimation problem by using the maximum likelihood estimation (MLE) principle and devise an effective and efficient algorithm to solve the optimization problem. Experimental results using EHR data of 234 HF patients validate the superiority of this new model in predicting prognosis over the currently used Seattle Heart Failure Model.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 410-416 |
| Number of pages | 7 |
| Volume | 2017-January |
| ISBN (Electronic) | 9781509030491 |
| DOIs | |
| State | Published - Dec 15 2017 |
| Event | 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States Duration: Nov 13 2017 → Nov 16 2017 |
Other
| Other | 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 |
|---|---|
| Country | United States |
| City | Kansas City |
| Period | 11/13/17 → 11/16/17 |
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Keywords
- Electronic Health Records
- Heart Failure (HF) Survival Score Prediction
- Logistic Regression
- Temporal Models
ASJC Scopus subject areas
- Biomedical Engineering
- Health Informatics
Cite this
Temporal reflected logistic regression for probabilistic heart failure survival score prediction. / Qian, Mingjie; Pathak, Jyotishman; Pereira, Naveen Luke; Zhai, Chengxiang.
Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 410-416.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Temporal reflected logistic regression for probabilistic heart failure survival score prediction
AU - Qian, Mingjie
AU - Pathak, Jyotishman
AU - Pereira, Naveen Luke
AU - Zhai, Chengxiang
PY - 2017/12/15
Y1 - 2017/12/15
N2 - Heart failure (HF) has a highly variable annual mortality rate and there is an urgent need of determining patient prognosis to enable informed decision-making about heart failure treatment strategies. Existing survival risk prediction models either require features that limit their applicability or pose difficulties for parameter estimation as physicians have to use a limited set of variables with known hazard ratios published in literature. We propose a new model to predict the probabilistic survival score after HF diagnosis based on all clinical variables derived from the electronic health record (EHR). We formalize the parameter estimation problem by using the maximum likelihood estimation (MLE) principle and devise an effective and efficient algorithm to solve the optimization problem. Experimental results using EHR data of 234 HF patients validate the superiority of this new model in predicting prognosis over the currently used Seattle Heart Failure Model.
AB - Heart failure (HF) has a highly variable annual mortality rate and there is an urgent need of determining patient prognosis to enable informed decision-making about heart failure treatment strategies. Existing survival risk prediction models either require features that limit their applicability or pose difficulties for parameter estimation as physicians have to use a limited set of variables with known hazard ratios published in literature. We propose a new model to predict the probabilistic survival score after HF diagnosis based on all clinical variables derived from the electronic health record (EHR). We formalize the parameter estimation problem by using the maximum likelihood estimation (MLE) principle and devise an effective and efficient algorithm to solve the optimization problem. Experimental results using EHR data of 234 HF patients validate the superiority of this new model in predicting prognosis over the currently used Seattle Heart Failure Model.
KW - Electronic Health Records
KW - Heart Failure (HF) Survival Score Prediction
KW - Logistic Regression
KW - Temporal Models
UR - http://www.scopus.com/inward/record.url?scp=85046262464&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046262464&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2017.8217684
DO - 10.1109/BIBM.2017.8217684
M3 - Conference contribution
AN - SCOPUS:85046262464
VL - 2017-January
SP - 410
EP - 416
BT - Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -