Individualized risk assessment model based on Bayesian networks and implementation by R software_Chinese Journal of Evidence-Based Medicine

Authors：

LIU Xingyu , RAO Qingyu , LIU Zuting , MO Jiali , YAN Chun , LIU Jin , DU Huijie , LU Yi ,  KUANG Jie

Department of Epidemiology, School of Public Health, Jiangxi Medical College, Nanchang University; Jiangxi Provincial Key Laboratory of Disease Prevention and Public Health, Nanchang University, Nanchang 330006, P. R. China;

Corresponding author：

KUANG Jie, Email: kuangjie@ncu.edu.cn

Keywords：

Bayesian network; Risk prediction; Model building

DOI：

10.7507/1672-2531.202405072

Video：

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This study introduced the construction of individualized risk assessment model based on Bayesian networks, comparing with traditional regression-based logistic models using practical examples. It evaluates the model's performance and demonstrates its implementation in the R software, serving as a valuable reference for researchers seeking to understand and utilize Bayesian network models.

1.	Moons KG, Royston P, Vergouwe Y, et al. Prognosis and prognostic research: what, why, and how. BMJ, 2009, 338: b375.
2.	Wang K, Shi Q, Sun C, et al. A machine learning model for visualization and dynamic clinical prediction of stroke recurrence in acute ischemic stroke patients: a real-world retrospective study. Front Neurosci, 2023, 17: 1130831.
3.	Tong L, Sun Y, Zhu Y, et al. Prognostic estimation for acute ischemic stroke patients undergoing mechanical thrombectomy within an extended therapeutic window using an interpretable machine learning model. Front Neuroinform, 2023, 17: 1273827.
4.	Handelman GS, Kok HK, Chandra RV, et al. eDoctor: machine learning and the future of medicine. J Intern Med, 2018, 284(6): 603-619.
5.	Choi RY, Coyner AS, Kalpathy-Cramer J, et al. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol, 2020, 9(2): 14.
6.	Song W, Qiu L, Qing J, et al. Using Bayesian network model with MMHC algorithm to detect risk factors for stroke. Math Biosci Eng, 2022, 19(12): 13660-13674.
7.	Park E, Chang HJ, Nam HS. A Bayesian network model for predicting post-stroke outcomes with available risk factors. Front Neurol, 2018, 9: 699.
8.	Suo X, Huang X, Zhong L, et al. Development and validation of a Bayesian network-based model for predicting coronary heart disease risk from electronic health records. J Am Heart Assoc, 2024, 13(1): e029400.
9.	Zhang H, Zhang X, Yao X, et al. Exploring factors related to heart attack complicated with hypertension using a Bayesian network model: a study based on the China Health and Retirement Longitudinal Study. Front Public Health, 2023, 11: 1259718.
10.	Pearl J. Fusion, propagation, and structuring in belief networks. Artif Intell, 1986, 29(3): 241-288.
11.	Bielza C, Larrañaga P. Bayesian networks in neuroscience: a survey. Front Comput Neurosci, 2014, 8: 131.
12.	Pan J, Rao H, Zhang X, et al. Application of a Tabu search-based Bayesian network in identifying factors related to hypertension. Medicine (Baltimore), 2019, 98(25): e16058.
13.	Zhang Z, Zhang J, Wei Z, et al. Application of tabu search-based Bayesian networks in exploring related factors of liver cirrhosis complicated with hepatic encephalopathy and disease identification. Sci Rep, 2019, 9(1): 6251.
14.	Arora P, Boyne D, Slater JJ, et al. Bayesian networks for risk prediction using real-world data: a tool for precision medicine. Value Health, 2019, 22(4): 439-445.
15.	Parviainen P, Kaski S. Learning structures of Bayesian networks for variable groups. J Approximate Reasoning, 2017, 88: 110-127.
16.	Fedesoriano. Stroke prediction dataset.
17.	Kim J, Mun S, Lee S, et al. Prediction of metabolic and pre-metabolic syndromes using machine learning models with anthropometric, lifestyle, and biochemical factors from a middle-aged population in Korea. BMC Public Health, 2022, 22(1): 664.
18.	Fan Y, Liu M, Sun G. An interpretable machine learning framework for diagnosis and prognosis of COVID-19. PLoS One, 2023, 18(9): e0291961.
19.	Yang L, Zheng Y, Liu Z, et al. SAR model for accurate detection of multi-label arrhythmias from electrocardiograms. Heliyon, 2023, 9(11): e21627.
20.	Ma L, Cai B, Qiao ML, et al. Risk factors assessment and a Bayesian network model for predicting ischemic stroke in patients with cardiac myxoma. Front Cardiovasc Med, 2023, 10: 1128022.
21.	Yuan B, Wang C, Fan Z, et al. A Bayesian network-based approach for identifying risk factors and predicting ischemic stroke in infective endocarditis patients. Front Cardiovasc Med, 2024, 10: 1294229.
22.	Fan ZX, Wang CB, Fang LB, et al. Risk factors and a Bayesian network model to predict ischemic stroke in patients with dilated cardiomyopathy. Front Neurosci, 2022, 16: 1043922.
23.	Butz CJ, Hua S, Chen J, et al. A simple graphical approach for understanding probabilistic inference in Bayesian networks. Inf Sci, 2009, 179(6): 699-716.
24.	Wang X, Pan J, Ren Z, et al. Application of a novel hybrid algorithm of Bayesian network in the study of hyperlipidemia related factors: a cross-sectional study. BMC Public Health, 2021, 21(1): 1375.
25.	Sandri M, Berchialla P, Baldi I, et al. Dynamic Bayesian networks to predict sequences of organ failures in patients admitted to ICU. J Biomed Inform, 2014, 48: 106-113.
26.	Yao X, Ouyang S, Lian Y, et al. PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies. Genome Med, 2024, 16(1): 56.

1. Moons KG, Royston P, Vergouwe Y, et al. Prognosis and prognostic research: what, why, and how. BMJ, 2009, 338: b375.
2. Wang K, Shi Q, Sun C, et al. A machine learning model for visualization and dynamic clinical prediction of stroke recurrence in acute ischemic stroke patients: a real-world retrospective study. Front Neurosci, 2023, 17: 1130831.
3. Tong L, Sun Y, Zhu Y, et al. Prognostic estimation for acute ischemic stroke patients undergoing mechanical thrombectomy within an extended therapeutic window using an interpretable machine learning model. Front Neuroinform, 2023, 17: 1273827.
4. Handelman GS, Kok HK, Chandra RV, et al. eDoctor: machine learning and the future of medicine. J Intern Med, 2018, 284(6): 603-619.
5. Choi RY, Coyner AS, Kalpathy-Cramer J, et al. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol, 2020, 9(2): 14.
6. Song W, Qiu L, Qing J, et al. Using Bayesian network model with MMHC algorithm to detect risk factors for stroke. Math Biosci Eng, 2022, 19(12): 13660-13674.
7. Park E, Chang HJ, Nam HS. A Bayesian network model for predicting post-stroke outcomes with available risk factors. Front Neurol, 2018, 9: 699.
8. Suo X, Huang X, Zhong L, et al. Development and validation of a Bayesian network-based model for predicting coronary heart disease risk from electronic health records. J Am Heart Assoc, 2024, 13(1): e029400.
9. Zhang H, Zhang X, Yao X, et al. Exploring factors related to heart attack complicated with hypertension using a Bayesian network model: a study based on the China Health and Retirement Longitudinal Study. Front Public Health, 2023, 11: 1259718.
10. Pearl J. Fusion, propagation, and structuring in belief networks. Artif Intell, 1986, 29(3): 241-288.
11. Bielza C, Larrañaga P. Bayesian networks in neuroscience: a survey. Front Comput Neurosci, 2014, 8: 131.
12. Pan J, Rao H, Zhang X, et al. Application of a Tabu search-based Bayesian network in identifying factors related to hypertension. Medicine (Baltimore), 2019, 98(25): e16058.
13. Zhang Z, Zhang J, Wei Z, et al. Application of tabu search-based Bayesian networks in exploring related factors of liver cirrhosis complicated with hepatic encephalopathy and disease identification. Sci Rep, 2019, 9(1): 6251.
14. Arora P, Boyne D, Slater JJ, et al. Bayesian networks for risk prediction using real-world data: a tool for precision medicine. Value Health, 2019, 22(4): 439-445.
15. Parviainen P, Kaski S. Learning structures of Bayesian networks for variable groups. J Approximate Reasoning, 2017, 88: 110-127.
16. Fedesoriano. Stroke prediction dataset.
17. Kim J, Mun S, Lee S, et al. Prediction of metabolic and pre-metabolic syndromes using machine learning models with anthropometric, lifestyle, and biochemical factors from a middle-aged population in Korea. BMC Public Health, 2022, 22(1): 664.
18. Fan Y, Liu M, Sun G. An interpretable machine learning framework for diagnosis and prognosis of COVID-19. PLoS One, 2023, 18(9): e0291961.
19. Yang L, Zheng Y, Liu Z, et al. SAR model for accurate detection of multi-label arrhythmias from electrocardiograms. Heliyon, 2023, 9(11): e21627.
20. Ma L, Cai B, Qiao ML, et al. Risk factors assessment and a Bayesian network model for predicting ischemic stroke in patients with cardiac myxoma. Front Cardiovasc Med, 2023, 10: 1128022.
21. Yuan B, Wang C, Fan Z, et al. A Bayesian network-based approach for identifying risk factors and predicting ischemic stroke in infective endocarditis patients. Front Cardiovasc Med, 2024, 10: 1294229.
22. Fan ZX, Wang CB, Fang LB, et al. Risk factors and a Bayesian network model to predict ischemic stroke in patients with dilated cardiomyopathy. Front Neurosci, 2022, 16: 1043922.
23. Butz CJ, Hua S, Chen J, et al. A simple graphical approach for understanding probabilistic inference in Bayesian networks. Inf Sci, 2009, 179(6): 699-716.
24. Wang X, Pan J, Ren Z, et al. Application of a novel hybrid algorithm of Bayesian network in the study of hyperlipidemia related factors: a cross-sectional study. BMC Public Health, 2021, 21(1): 1375.
25. Sandri M, Berchialla P, Baldi I, et al. Dynamic Bayesian networks to predict sequences of organ failures in patients admitted to ICU. J Biomed Inform, 2014, 48: 106-113.
26. Yao X, Ouyang S, Lian Y, et al. PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies. Genome Med, 2024, 16(1): 56.

Chinese Journal of Evidence-Based Medicine

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