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) |
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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 |
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Country | United States |
City | Kansas City |
Period | 11/13/17 → 11/16/17 |
Keywords
- Electronic Health Records
- Heart Failure (HF) Survival Score Prediction
- Logistic Regression
- Temporal Models
ASJC Scopus subject areas
- Biomedical Engineering
- Health Informatics