title: Developing EHR-driven heart failure risk prediction models using CPXR(Log) with the probabilistic loss function.
abstract: Computerized survival prediction in healthcare identifying the risk of disease mortality, helps healthcare providers to effectively manage their patients by providing appropriate treatment options. In this study, we propose 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 5year survival in heart failure (HF) using data from electronic health records (EHRs) at Mayo Clinic. The CPXR(Log) constructs a pattern aided logistic regression model defined by several patterns and corresponding local logistic regression models. One of the models generated by CPXR(Log) achieved an AUC and accuracy of 0.94 and 0.91, respectively, and significantly outperformed prognostic models reported in prior studies. Data extracted from EHRs allowed incorporation of patient co-morbidities into our models which helped improve the performance of the CPXR(Log) models (15.9% AUC improvement), although did not improve the accuracy of the models built by other classifiers. We also propose a probabilistic loss function to determine the large error and small error instances. The new loss function used in the algorithm outperforms other functions used in the previous studies by 1% improvement in the AUC. This study revealed that using EHR data to build prediction models can be very challenging using existing classification methods due to the high dimensionality and complexity of EHR data. The risk models developed by CPXR(Log) also reveal that HF is a highly heterogeneous disease, i.e., different subgroups of HF patients require different types of considerations with their diagnosis and treatment. Our risk models provided two valuable insights for application of predictive modeling techniques in biomedicine: Logistic risk models often make systematic prediction errors, and it is prudent to use subgroup based prediction models such as those given by CPXR(Log) when investigating heterogeneous diseases.
authors: Department of Computer Science and Engineering, Kno.e.sis Center, Wright State University, Dayton, OH, USA; Division of Health Informatics, Weill Cornell Medical College, New York, NY, USA. Electronic address: taslimitehrani.2@wright.edu.
authors: Vahid
authors: V
authors: Taslimitehrani
authors: Department of Computer Science and Engineering, Kno.e.sis Center, Wright State University, Dayton, OH, USA.
authors: Guozhu
authors: G
authors: Dong
authors: Division of Cardiovascular Diseases and Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.
authors: Naveen L
authors: NL
authors: Pereira
authors: Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University, CA, USA.
authors: Maryam
authors: M
authors: Panahiazar
authors: Division of Health Informatics, Weill Cornell Medical College, New York, NY, USA.
authors: Jyotishman
authors: J
authors: Pathak
mesh_terms: D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D015331:Cohort Studies; D015897:Comorbidity; D057286:Electronic Health Records; D005189:False Positive Reactions; D005260:Female; D006333:Heart Failure / Q000175:diagnosis*; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D008297:Male; D008490:Medical Informatics / Q000379:methods*; D008875:Middle Aged; D011336:Probability; D011379:Prognosis; D012372:ROC Curve; D012044:Regression Analysis; D015203:Reproducibility of Results; D012306:Risk
keywords: Contrast pattern aided logistic regression; Heart failure; Predictive modeling; Survival analysis