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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 1 of 1 Research Studies DisplayedTaslimitehrani V, Dong G, Pereira NL
Developing EHR-driven heart failure risk prediction models using CPXR(Log) with the probabilistic loss function.
The authors proposed to apply a classification algorithm, Contrast Pattern Aided Logistic Regression (CPXR(Log)) with the probabilistic loss function, to develop and validate prognostic risk models to predict 1, 2, and 5 year survival in heart failure (HF). They found that the new loss function used in the algorithm outperforms other functions used in previous studies and that HF is a highly heterogeneous disease (different subgroups of patients require different types of considerations with their diagnosis and treatment). They concluded that logistic risk models often make systematic prediction errors and that it is prudent to use subgroup based prediction models such as those given by CPXR(Log) when investigating heterogeneous diseases.
AHRQ-funded; HS023077.
Citation: Taslimitehrani V, Dong G, Pereira NL .
Developing EHR-driven heart failure risk prediction models using CPXR(Log) with the probabilistic loss function.
J Biomed Inform 2016 Apr;60:260-9. doi: 10.1016/j.jbi.2016.01.009.
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Keywords: Electronic Health Records (EHRs), Heart Disease and Health, Health Information Technology (HIT), Risk