Bayesian Network vs. Cox’s Proportional Hazard Model of PAH Risk: A Comparison

Kraisangka, Jidapa; Drużdżel, Marek J.; Lohmueller, Lisa; Kanwar, Manreet; Antaki, James F.; Benza, Raymond L.

doi:10.1007/978-3-030-21642-9_19

Cited by 3 publications

(7 citation statements)

References 12 publications

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“…They can account for dynamic, nonlinear interactions between multiple variables and their interdependency in influencing outcomes at various time points. These networks can encode both qualitative and quantitative knowledge, can be represented diagrammatically or numerically and provide a rigorous framework to perform inferences from predictive variables [8]. In this article, we sought to demonstrate the utility of Bayesian network-based machine learning in enhancing the predictive ability of a contemporary risk stratification tool, REVEAL 2.0 [9].…”

Section: Introductionmentioning

confidence: 99%

Risk stratification in pulmonary arterial hypertension using Bayesian analysis

et al. 2020

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BackgroundCurrent risk stratification tools in pulmonary arterial hypertension (PAH) are limited in their discriminatory abilities, partly due to the assumption that prognostic clinical variables have an independent and linear relationship to clinical outcomes. We sought to demonstrate the utility of Bayesian network (BN) based machine learning in enhancing the predictive ability of an existing state-of-the-art risk stratification tool, REVEAL 2.0.MethodsWe derived a Tree Augmented Naïve Bayes model (titled PHORA) to predict one-year survival in PAH patients included in the REVEAL registry, using the same variables and cut-points found in REVEAL 2.0. PHORA models were validated internally (within the REVEAL registry) and externally (in COMPERA and PHSANZ registry). Patients were classified as low, intermediate and high-risk (<5%, 5-20% and>10% 12-month mortality, respectively) based on the 2015 ESC/ERS guidelines.ResultsPHORA had an AUC of 0.80 for predicting one-year survival, which was an improvement over REVEAL 2.0 (AUC of 0.76). When validated in COMPERA and PHSANZ registries, PHORA demonstrated an AUC of 0.74 and 0.80 respectively. One-year survival rates predicted by PHORA were greater for patients with lower risk scores and poorer for those with higher risk scores (P<.001), with excellent separation between low-, intermediate-, and high-risk groups in all three registries.ConclusionOur BN derived risk prediction model, PHORA, demonstrated an improvement in discrimination over existing models. This is reflective of BN based model's ability to account for the interrelationships between clinical variables on outcome, and tolerance to missing data elements when calculating predictions.

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Section: Introductionmentioning

confidence: 99%

Risk stratification in pulmonary arterial hypertension using Bayesian analysis

et al. 2020

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“…The application of data-science methods to electronic health record (EHR) databases promises a new, global perspective on human health, with widespread applications for outcomes research and precision medicine initiatives. However, unmet technological challenges still exist [1][2][3] [ . One is the need for improved means for ab initio discovery of comorbid clinical variables in the context of confounding demographic variables at scale.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, because researchers seek not merely to predict outcomes, but also to measure the contributions of factors driving them, 'explainable' solutions [14][15][16][17][18][19][20][21][22], rather than black box approaches are required. We have adapted Probabilistic Graphical Models (PGMs) [2,[22][23][24][25][26][27] to address these needs.…”

Section: Introductionmentioning

confidence: 99%

An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records

et al. 2022

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Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children’s Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications. Using explainable Artificial Intelligence (AI) methodologies, we then tease apart the intertwined, conditionally-dependent impacts of comorbid conditions and demography upon cardiovascular health, focusing on the key areas of heart transplant, sinoatrial node dysfunction and various forms of congenital heart disease. The resulting multimorbidity networks make possible wide-ranging explorations of the comorbid and demographic landscapes surrounding these cardiovascular outcomes, and can be distributed as web-based tools for further community-based outcomes research. The ability to transform enormous collections of EHRs into compact, portable tools devoid of Protected Health Information solves many of the legal, technological, and data-scientific challenges associated with large-scale EHR analyses.

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“…Moreover, because researchers seek not merely to predict outcomes, but also to measure the contributions of factors driving them, 'explainable' solutions [14][15][16][17][18][19][20][21][22] , rather than black box approaches are required. We have adapted Probabilistic Graphical Models (PGMs) 3,[22][23][24][25][26][27] to address these needs.…”

Section: Introductionmentioning

confidence: 99%

“…The application of data-science methods to electronic health record (EHR) databases promises a new, global perspective on human health, with widespread applications for outcomes research and precision medicine initiatives. However, unmet technological challenges still exist [1][2][3] . One is the need for improved means for ab initio discovery of comorbid clinical variables in the context of confounding demographic variables at scale.…”

Section: Introductionmentioning

confidence: 99%

An Explainable Artificial Intelligence Approach for Predicting Cardiovascular Outcomes using Electronic Health Records

Wesołowski

Lemmon

Hernández

et al. 2021

Preprint

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Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications. Using explainable Artificial Intelligence (AI) methodologies, we then tease apart the intertwined, conditionally-dependent impacts of comorbid conditions and demography upon cardiovascular health, focusing on the key areas of heart transplant, sinoatrial node dysfunction and various forms of congenital heart disease. The resulting multimorbidity networks make possible wide-ranging explorations of the comorbid and demographic landscapes surrounding these cardiovascular outcomes, and can be distributed as web-based tools for further community-based outcomes research. The ability to transform enormous collections of EHRs into compact, portable tools devoid of Protected Health Information solves many of the legal, technological, and data-scientific challenges associated with large-scale EHR analyzes.

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Bayesian Network vs. Cox’s Proportional Hazard Model of PAH Risk: A Comparison

Cited by 3 publications

References 12 publications

Risk stratification in pulmonary arterial hypertension using Bayesian analysis

Risk stratification in pulmonary arterial hypertension using Bayesian analysis

An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records

An Explainable Artificial Intelligence Approach for Predicting Cardiovascular Outcomes using Electronic Health Records

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