Developing EHR-driven heart failure risk prediction models using CPXR(Log) with the probabilistic loss function.
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 Regress...
| Publicado en: | Journal of Biomedical Informatics Vol. 60; pp. 260 - 270 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Apr2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=114629830&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 114629830 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Apr2016 vid: 60 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 114629830 114629830 NLM26844760 114629830 10.1016/j.jbi.2016.01.009 NLM26844760 PMC4886658 [Available on 04/01/17] 114629830 ppf: 260 ppct: 10 formats: tig: atl: Developing EHR-driven heart failure risk prediction models using CPXR(Log) with the probabilistic loss function. aug: au: Taslimitehrani, Vahid Dong, Guozhu Pereira, Naveen L. Panahiazar, Maryam Pathak, Jyotishman affil: Department of Computer Science and Engineering, Kno.e.sis Center, Wright State University, Dayton, OH, USA sug: subj: Heart Failure Diagnosis Medical Informatics Methods False Positive Results Prognosis Human Probability Reproducibility of Results Prospective Studies Male Algorithms Comorbidity Relative Risk Aged ROC Curve Middle Age Linear Regression Logistic Regression Regression Pharmacokinetics Female Validation Studies Comparative Studies Evaluation Research Multicenter Studies Aged: 65+ years Middle Aged: 45-64 years Male Female ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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